var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://api.zotero.org/users/6655/collections/TJPPJ92X/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100\",\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"The MovieLens Datasets: History and Context\",\"volume\":\"5\",\"issn\":\"2160-6455\",\"url\":\"http://doi.acm.org/10.1145/2827872\",\"doi\":\"10.1145/2827872\",\"abstract\":\"The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.\",\"number\":\"4\",\"urldate\":\"2016-03-11\",\"journal\":\"ACM Transactions on Interactive Intelligent Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Harper\"],\"firstnames\":[\"F\",\"Maxwell\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Konstan\"],\"firstnames\":[\"Joseph\",\"A\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2015\",\"keywords\":\"dataset\",\"pages\":\"19:1–19:19\",\"bibtex\":\"@article{harper_movielens_2015,\\n\\ttitle = {The {MovieLens} {Datasets}: {History} and {Context}},\\n\\tvolume = {5},\\n\\tissn = {2160-6455},\\n\\turl = {http://doi.acm.org/10.1145/2827872},\\n\\tdoi = {10.1145/2827872},\\n\\tabstract = {The MovieLens datasets are widely used in education, research, and\\nindustry. They are downloaded hundreds of thousands of times each year,\\nreflecting their use in popular press programming books, traditional and\\nonline courses, and software. These datasets are a product of member\\nactivity in the MovieLens movie recommendation system, an active research\\nplatform that has hosted many experiments since its launch in 1997. This\\narticle documents the history of MovieLens and the MovieLens datasets. We\\ninclude a discussion of lessons learned from running a long-standing, live\\nresearch platform from the perspective of a research organization. We\\ndocument best practices and limitations of using the MovieLens datasets in\\nnew research.},\\n\\tnumber = {4},\\n\\turldate = {2016-03-11},\\n\\tjournal = {ACM Transactions on Interactive Intelligent Systems},\\n\\tauthor = {Harper, F Maxwell and Konstan, Joseph A},\\n\\tmonth = dec,\\n\\tyear = {2015},\\n\\tkeywords = {dataset},\\n\\tpages = {19:1--19:19},\\n}\\n\\n\",\"author_short\":[\"Harper, F M.\",\"Konstan, J. A\"],\"key\":\"harper_movielens_2015\",\"id\":\"harper_movielens_2015\",\"bibbaseid\":\"harper-konstan-themovielensdatasetshistoryandcontext-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2827872\"},\"keyword\":[\"dataset\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"series\":\"Proceedings of Machine Learning Research\",\"title\":\"All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness\",\"volume\":\"81\",\"url\":\"https://proceedings.mlr.press/v81/ekstrand18b.html\",\"abstract\":\"In the research literature, evaluations of recommender system effectiveness typically report results over a given data set, providing an aggregate measure of effectiveness over each instance (e.g. user) in the data set. Recent advances in information retrieval evaluation, however, demonstrate the importance of considering the distribution of effectiveness across diverse groups of varying sizes. For example, do users of different ages or genders obtain similar utility from the system, particularly if their group is a relatively small subset of the user base? We apply this consideration to recommender systems, using offline evaluation and a utility-based metric of recommendation effectiveness to explore whether different user demographic groups experience similar recommendation accuracy. We find demographic differences in measured recommender effectiveness across two data sets containing different types of feedback in different domains; these differences sometimes, but not always, correlate with the size of the user group in question. Demographic effects also have a complex—and likely detrimental—interaction with popularity bias, a known deficiency of recommender evaluation. These results demonstrate the need for recommender system evaluation protocols that explicitly quantify the degree to which the system is meeting the information needs of all its users, as well as the need for researchers and operators to move beyond naïve evaluations that favor the needs of larger subsets of the user population while ignoring smaller subsets.\",\"booktitle\":\"Proceedings of the 1st Conference on Fairness, Accountability and Transparency\",\"publisher\":\"PMLR\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tian\"],\"firstnames\":[\"Mucun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Azpiazu\"],\"firstnames\":[\"Ion\",\"Madrazo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Jennifer\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Anuyah\"],\"firstnames\":[\"Oghenemaro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McNeill\"],\"firstnames\":[\"David\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pera\"],\"firstnames\":[\"Maria\",\"Soledad\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Friedler\"],\"firstnames\":[\"Sorelle\",\"A\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wilson\"],\"firstnames\":[\"Christo\"],\"suffixes\":[]}],\"year\":\"2018\",\"note\":\"Journal Abbreviation: Proceedings of Machine Learning Research\",\"pages\":\"172–186\",\"bibtex\":\"@inproceedings{ekstrand_all_2018,\\n\\tseries = {Proceedings of {Machine} {Learning} {Research}},\\n\\ttitle = {All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness},\\n\\tvolume = {81},\\n\\turl = {https://proceedings.mlr.press/v81/ekstrand18b.html},\\n\\tabstract = {In the research literature, evaluations of recommender system\\neffectiveness typically report results over a given data set, providing an\\naggregate measure of effectiveness over each instance (e.g. user) in the\\ndata set. Recent advances in information retrieval evaluation, however,\\ndemonstrate the importance of considering the distribution of\\neffectiveness across diverse groups of varying sizes. For example, do\\nusers of different ages or genders obtain similar utility from the system,\\nparticularly if their group is a relatively small subset of the user base?\\nWe apply this consideration to recommender systems, using offline\\nevaluation and a utility-based metric of recommendation effectiveness to\\nexplore whether different user demographic groups experience similar\\nrecommendation accuracy. We find demographic differences in measured\\nrecommender effectiveness across two data sets containing different types\\nof feedback in different domains; these differences sometimes, but not\\nalways, correlate with the size of the user group in question. Demographic\\neffects also have a complex—and likely detrimental—interaction with\\npopularity bias, a known deficiency of recommender evaluation. These\\nresults demonstrate the need for recommender system evaluation protocols\\nthat explicitly quantify the degree to which the system is meeting the\\ninformation needs of all its users, as well as the need for researchers\\nand operators to move beyond naïve evaluations that favor the needs of\\nlarger subsets of the user population while ignoring smaller subsets.},\\n\\tbooktitle = {Proceedings of the 1st {Conference} on {Fairness}, {Accountability} and {Transparency}},\\n\\tpublisher = {PMLR},\\n\\tauthor = {Ekstrand, Michael D and Tian, Mucun and Azpiazu, Ion Madrazo and Ekstrand, Jennifer D and Anuyah, Oghenemaro and McNeill, David and Pera, Maria Soledad},\\n\\teditor = {Friedler, Sorelle A and Wilson, Christo},\\n\\tyear = {2018},\\n\\tnote = {Journal Abbreviation: Proceedings of Machine Learning Research},\\n\\tpages = {172--186},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\",\"Tian, M.\",\"Azpiazu, I. M.\",\"Ekstrand, J. D\",\"Anuyah, O.\",\"McNeill, D.\",\"Pera, M. S.\"],\"editor_short\":[\"Friedler, S. A\",\"Wilson, C.\"],\"key\":\"ekstrand_all_2018\",\"id\":\"ekstrand_all_2018\",\"bibbaseid\":\"ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://proceedings.mlr.press/v81/ekstrand18b.html\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"series\":\"FLAIRS 30\",\"title\":\"Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation\",\"url\":\"https://aaai.org/papers/639-flairs-2017-15534/\",\"abstract\":\"Top-N evaluation of recommender systems, typically carried out using metrics from information retrieval or machine learning, has several challenges. Two of these challenges are popularity bias, where the evaluation intrinsically favors algorithms that recommend popular items, and misclassified decoys, where items for which no user relevance is known are actually relevant to the user, but the evaluation is unaware and penalizes the recommender for suggesting them. One strategy for mitigating the misclassified decoy problem is the one-plus-random evaluation strategy and its generalization, which we call random decoys. In this work, we explore the random decoy strategy through both a theoretical treatment and an empirical study, but find little evidence to guide its tuning and show that it has complex and deleterious interactions with popularity bias.\",\"booktitle\":\"Proceedings of the 30th Florida Artificial Intelligence Research Society Conference\",\"publisher\":\"AAAI Press\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mahant\"],\"firstnames\":[\"Vaibhav\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2017\",\"bibtex\":\"@inproceedings{ekstrand_sturgeon_2017,\\n\\tseries = {{FLAIRS} 30},\\n\\ttitle = {Sturgeon and the {Cool} {Kids}: {Problems} with {Top}-{N} {Recommender} {Evaluation}},\\n\\turl = {https://aaai.org/papers/639-flairs-2017-15534/},\\n\\tabstract = {Top-N evaluation of recommender systems, typically carried out using\\nmetrics from information retrieval or machine learning, has several\\nchallenges. Two of these challenges are popularity bias, where the\\nevaluation intrinsically favors algorithms that recommend popular items,\\nand misclassified decoys, where items for which no user relevance is known\\nare actually relevant to the user, but the evaluation is unaware and\\npenalizes the recommender for suggesting them. One strategy for mitigating\\nthe misclassified decoy problem is the one-plus-random evaluation strategy\\nand its generalization, which we call random decoys. In this work, we\\nexplore the random decoy strategy through both a theoretical treatment and\\nan empirical study, but find little evidence to guide its tuning and show\\nthat it has complex and deleterious interactions with popularity bias.},\\n\\tbooktitle = {Proceedings of the 30th {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}},\\n\\tpublisher = {AAAI Press},\\n\\tauthor = {Ekstrand, Michael D and Mahant, Vaibhav},\\n\\tmonth = may,\\n\\tyear = {2017},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\",\"Mahant, V.\"],\"key\":\"ekstrand_sturgeon_2017\",\"id\":\"ekstrand_sturgeon_2017\",\"bibbaseid\":\"ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://aaai.org/papers/639-flairs-2017-15534/\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Recommender response to diversity and popularity bias in user profiles\",\"url\":\"https://aaai.org/papers/657-flairs-2017-15524/\",\"abstract\":\"Recommender system evaluation usually focuses on the overall effectiveness of the algorithms, either in terms of measurable accuracy or ability to deliver user satisfaction or improve business metrics. When additional factors are considered, such as the diversity or novelty of the recommendations, the focus typically remains on the algorithm’s overall performance. We examine the relationship of the recommender’s output characteristics – accuracy, popularity (as an inverse of novelty), and diversity – to characteristics of the user’s rating profile. The aims of this analysis are twofold: (1) to probe the conditions under which common algorithms produce more or less diverse or popular recommendations, and (2) to determine if these personalized recommender algorithms reflect a user’s preference for diversity or novelty. We trained recommenders on the MovieLens data and looked for correlation between the user profile and the recommender’s output for both diversity and popularity bias using different metrics. We find that the diversity and popularity of movies in users’ profiles has little impact on the recommendations they receive.\",\"urldate\":\"2017-05-29\",\"booktitle\":\"Proceedings of the 30th Florida artificial intelligence research society conference\",\"publisher\":\"AAAI Press\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Channamsetty\"],\"firstnames\":[\"Sushma\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2017\",\"bibtex\":\"@inproceedings{channamsetty_recommender_2017,\\n\\ttitle = {Recommender response to diversity and popularity bias in user profiles},\\n\\turl = {https://aaai.org/papers/657-flairs-2017-15524/},\\n\\tabstract = {Recommender system evaluation usually focuses on the overall effectiveness\\nof the algorithms, either in terms of measurable accuracy or ability to\\ndeliver user satisfaction or improve business metrics. When additional\\nfactors are considered, such as the diversity or novelty of the\\nrecommendations, the focus typically remains on the algorithm’s overall\\nperformance. We examine the relationship of the recommender’s output\\ncharacteristics – accuracy, popularity (as an inverse of novelty), and\\ndiversity – to characteristics of the user’s rating profile. The aims of\\nthis analysis are twofold: (1) to probe the conditions under which common\\nalgorithms produce more or less diverse or popular recommendations, and\\n(2) to determine if these personalized recommender algorithms reflect a\\nuser’s preference for diversity or novelty. We trained recommenders on the\\nMovieLens data and looked for correlation between the user profile and the\\nrecommender’s output for both diversity and popularity bias using\\ndifferent metrics. We find that the diversity and popularity of movies in\\nusers’ profiles has little impact on the recommendations they receive.},\\n\\turldate = {2017-05-29},\\n\\tbooktitle = {Proceedings of the 30th {Florida} artificial intelligence research society conference},\\n\\tpublisher = {AAAI Press},\\n\\tauthor = {Channamsetty, Sushma and Ekstrand, Michael D},\\n\\tmonth = may,\\n\\tyear = {2017},\\n}\\n\\n\",\"author_short\":[\"Channamsetty, S.\",\"Ekstrand, M. D\"],\"key\":\"channamsetty_recommender_2017\",\"id\":\"channamsetty_recommender_2017\",\"bibbaseid\":\"channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://aaai.org/papers/657-flairs-2017-15524/\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Exploring author gender in book rating and recommendation\",\"url\":\"https://dl.acm.org/doi/10.1145/3240323.3240373\",\"doi\":\"10.1145/3240323.3240373\",\"abstract\":\"Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tian\"],\"firstnames\":[\"Mucun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kazi\"],\"firstnames\":[\"Mohammed\",\"R\",\"Imran\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mehrpouyan\"],\"firstnames\":[\"Hoda\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kluver\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2018\",\"bibtex\":\"@inproceedings{ekstrand_exploring_2018,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Exploring author gender in book rating and recommendation},\\n\\turl = {https://dl.acm.org/doi/10.1145/3240323.3240373},\\n\\tdoi = {10.1145/3240323.3240373},\\n\\tabstract = {Collaborative filtering algorithms find useful patterns in rating and\\nconsumption data and exploit these patterns to guide users to good items.\\nMany of the patterns in rating datasets reflect important real-world\\ndifferences between the various users and items in the data; other\\npatterns may be irrelevant or possibly undesirable for social or ethical\\nreasons, particularly if they reflect undesired discrimination, such as\\ngender or ethnic discrimination in publishing. In this work, we examine\\nthe response of collaborative filtering recommender algorithms to the\\ndistribution of their input data with respect to a dimension of social\\nconcern, namely content creator gender. Using publicly-available book\\nratings data, we measure the distribution of the genders of the authors of\\nbooks in user rating profiles and recommendation lists produced from this\\ndata. We find that common collaborative filtering algorithms differ in the\\ngender distribution of their recommendation lists, and in the relationship\\nof that output distribution to user profile distribution.},\\n\\tpublisher = {ACM},\\n\\tauthor = {Ekstrand, Michael D and Tian, Mucun and Kazi, Mohammed R Imran and Mehrpouyan, Hoda and Kluver, Daniel},\\n\\tmonth = sep,\\n\\tyear = {2018},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\",\"Tian, M.\",\"Kazi, M. R I.\",\"Mehrpouyan, H.\",\"Kluver, D.\"],\"key\":\"ekstrand_exploring_2018\",\"id\":\"ekstrand_exploring_2018\",\"bibbaseid\":\"ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3240323.3240373\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"From recommendation to curation: when the system becomes your personal docent\",\"url\":\"http://ceur-ws.org/Vol-2225/paper6.pdf\",\"abstract\":\"Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a …\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Dragovic\"],\"firstnames\":[\"Nevena\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Azpiazu\"],\"firstnames\":[\"Ion\",\"Madrazo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pera\"],\"firstnames\":[\"Maria\",\"Soledad\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2018\",\"pages\":\"37–44\",\"bibtex\":\"@inproceedings{dragovic_recommendation_2018,\\n\\ttitle = {From recommendation to curation: when the system becomes your personal docent},\\n\\turl = {http://ceur-ws.org/Vol-2225/paper6.pdf},\\n\\tabstract = {Curation is the act of selecting, organizing, and presenting content. Some\\napplications emulate this process by turning users into curators, while\\nothers use recommenders to select items, seldom achieving the focus or\\nselectivity of human curators. We bridge this gap with a …},\\n\\tauthor = {Dragovic, Nevena and Azpiazu, Ion Madrazo and Pera, Maria Soledad},\\n\\tmonth = oct,\\n\\tyear = {2018},\\n\\tpages = {37--44},\\n}\\n\\n\",\"author_short\":[\"Dragovic, N.\",\"Azpiazu, I. M.\",\"Pera, M. S.\"],\"key\":\"dragovic_recommendation_2018\",\"id\":\"dragovic_recommendation_2018\",\"bibbaseid\":\"dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-2225/paper6.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"unpublished\",\"type\":\"unpublished\",\"title\":\"The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens\",\"url\":\"http://arxiv.org/abs/2211.14219\",\"abstract\":\"We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations increase consumption beyond its role in exposing goods to consumers. We provide support for an informational mechanism: recommendations affect consumers' beliefs, which in turn explain consumption. Recommendations reduce uncertainty about goods consumers are most uncertain about and induce information acquisition. Our results highlight the importance of recommender systems' informational role when considering policies targeting these systems in online marketplaces.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Aridor\"],\"firstnames\":[\"Guy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Goncalves\"],\"firstnames\":[\"Duarte\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kluver\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kong\"],\"firstnames\":[\"Ruoyan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Konstan\"],\"firstnames\":[\"Joseph\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"note\":\"ISBN: 2211.14219 Publication Title: arXiv [econ.GN]\",\"bibtex\":\"@unpublished{aridor_economics_2022,\\n\\ttitle = {The {Economics} of {Recommender} {Systems}: {Evidence} from a {Field} {Experiment} on {MovieLens}},\\n\\turl = {http://arxiv.org/abs/2211.14219},\\n\\tabstract = {We conduct a field experiment on a movie-recommendation platform to\\nidentify if and how recommendations affect consumption. We use\\nwithin-consumer randomization at the good level and elicit beliefs about\\nunconsumed goods to disentangle exposure from informational effects. We\\nfind recommendations increase consumption beyond its role in exposing\\ngoods to consumers. We provide support for an informational mechanism:\\nrecommendations affect consumers' beliefs, which in turn explain\\nconsumption. Recommendations reduce uncertainty about goods consumers are\\nmost uncertain about and induce information acquisition. Our results\\nhighlight the importance of recommender systems' informational role when\\nconsidering policies targeting these systems in online marketplaces.},\\n\\tauthor = {Aridor, Guy and Goncalves, Duarte and Kluver, Daniel and Kong, Ruoyan and Konstan, Joseph},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n\\tnote = {ISBN: 2211.14219\\nPublication Title: arXiv [econ.GN]},\\n}\\n\\n\",\"author_short\":[\"Aridor, G.\",\"Goncalves, D.\",\"Kluver, D.\",\"Kong, R.\",\"Konstan, J.\"],\"key\":\"aridor_economics_2022\",\"id\":\"aridor_economics_2022\",\"bibbaseid\":\"aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2211.14219\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Hybrid Recommender for Research Papers and Articles\",\"volume\":\"10\",\"url\":\"http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf\",\"abstract\":\"… GroupLens called LensKit , along with set of tools for such system was used to implement Collaborative filtering algorithm. This research uses only the LensKit -core and LensKit -data-structures modules to implement this section of the algorithm …\",\"number\":\"2\",\"journal\":\"Int. J. Intell. Inf. Database Syst.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ibrahim\"],\"firstnames\":[\"Alhassan\",\"Jamilu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zira\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Abdulganiyyi\"],\"firstnames\":[\"Nuraini\"],\"suffixes\":[]}],\"year\":\"2021\",\"note\":\"Publisher: Science Publishing Group\",\"pages\":\"9\",\"bibtex\":\"@article{ibrahim_hybrid_2021,\\n\\ttitle = {Hybrid {Recommender} for {Research} {Papers} and {Articles}},\\n\\tvolume = {10},\\n\\turl = {http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf},\\n\\tabstract = {… GroupLens called LensKit , along with set of tools for such system was\\nused to implement Collaborative filtering algorithm. This research uses\\nonly the LensKit -core and LensKit -data-structures modules to implement\\nthis section of the algorithm …},\\n\\tnumber = {2},\\n\\tjournal = {Int. J. Intell. Inf. Database Syst.},\\n\\tauthor = {Ibrahim, Alhassan Jamilu and Zira, Peter and Abdulganiyyi, Nuraini},\\n\\tyear = {2021},\\n\\tnote = {Publisher: Science Publishing Group},\\n\\tpages = {9},\\n}\\n\\n\",\"author_short\":[\"Ibrahim, A. J.\",\"Zira, P.\",\"Abdulganiyyi, N.\"],\"key\":\"ibrahim_hybrid_2021\",\"id\":\"ibrahim_hybrid_2021\",\"bibbaseid\":\"ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Recommender Systems for Software Project Managers\",\"url\":\"https://doi.org/10.1145/3463274.3463951\",\"doi\":\"10.1145/3463274.3463951\",\"abstract\":\"The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.\",\"urldate\":\"2021-09-14\",\"booktitle\":\"EASE 2021\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wei\"],\"firstnames\":[\"Liang\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Capretz\"],\"firstnames\":[\"Luiz\",\"Fernando\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2021\",\"note\":\"Journal Abbreviation: EASE 2021\",\"keywords\":\"RSSE, Recommender Engine, Project Management, Recommendation System, Recommendation System in Software Engineering\",\"pages\":\"412–417\",\"bibtex\":\"@inproceedings{wei_recommender_2021,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Recommender {Systems} for {Software} {Project} {Managers}},\\n\\turl = {https://doi.org/10.1145/3463274.3463951},\\n\\tdoi = {10.1145/3463274.3463951},\\n\\tabstract = {The design of recommendation systems is based on complex information\\nprocessing and big data interaction. This personalized view has evolved\\ninto a hot area in the past decade, where applications might have been\\nproved to help for solving problem in the software development field.\\nTherefore, with the evolvement of Recommendation System in Software\\nEngineering (RSSE), the coordination of software projects with their\\nstakeholders is improving. This experiment examines four open source\\nrecommender systems and implemented a customized recommender engine with\\ntwo industrial-oriented packages: Lenskit and Mahout. Each of the main\\nfunctions was examined and issues were identified during the experiment.},\\n\\turldate = {2021-09-14},\\n\\tbooktitle = {{EASE} 2021},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Wei, Liang and Capretz, Luiz Fernando},\\n\\tmonth = jun,\\n\\tyear = {2021},\\n\\tnote = {Journal Abbreviation: EASE 2021},\\n\\tkeywords = {RSSE, Recommender Engine, Project Management, Recommendation System, Recommendation System in Software Engineering},\\n\\tpages = {412--417},\\n}\\n\\n\",\"author_short\":[\"Wei, L.\",\"Capretz, L. F.\"],\"key\":\"wei_recommender_2021\",\"id\":\"wei_recommender_2021\",\"bibbaseid\":\"wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1145/3463274.3463951\"},\"keyword\":[\"RSSE\",\"Recommender Engine\",\"Project Management\",\"Recommendation System\",\"Recommendation System in Software Engineering\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Privacy and performance in recommender systems: Exploration of potential influence of CCPA\",\"url\":\"http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf\",\"urldate\":\"2021-07-12\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Zhou\"],\"firstnames\":[\"Meizi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Song\"],\"firstnames\":[\"Yicheng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Adomavicius\"],\"firstnames\":[\"Gediminas\"],\"suffixes\":[]}],\"year\":\"2021\",\"bibtex\":\"@inproceedings{zhou_privacy_2021,\\n\\ttitle = {Privacy and performance in recommender systems: {Exploration} of potential influence of {CCPA}},\\n\\turl = {http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf},\\n\\turldate = {2021-07-12},\\n\\tauthor = {Zhou, Meizi and Song, Yicheng and Adomavicius, Gediminas},\\n\\tyear = {2021},\\n}\\n\\n\",\"author_short\":[\"Zhou, M.\",\"Song, Y.\",\"Adomavicius, G.\"],\"key\":\"zhou_privacy_2021\",\"id\":\"zhou_privacy_2021\",\"bibbaseid\":\"zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Engaging end-user driven recommender systems: personalization through web augmentation\",\"volume\":\"80\",\"issn\":\"1380-7501\",\"url\":\"https://doi.org/10.1007/s11042-020-09803-8\",\"doi\":\"10.1007/s11042-020-09803-8\",\"abstract\":\"In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.\",\"number\":\"5\",\"journal\":\"Multimed. Tools Appl.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wischenbart\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Firmenich\"],\"firstnames\":[\"Sergio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rossi\"],\"firstnames\":[\"Gustavo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bosetti\"],\"firstnames\":[\"Gabriela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kapsammer\"],\"firstnames\":[\"Elisabeth\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2021\",\"pages\":\"6785–6809\",\"bibtex\":\"@article{wischenbart_engaging_2021,\\n\\ttitle = {Engaging end-user driven recommender systems: personalization through web augmentation},\\n\\tvolume = {80},\\n\\tissn = {1380-7501},\\n\\turl = {https://doi.org/10.1007/s11042-020-09803-8},\\n\\tdoi = {10.1007/s11042-020-09803-8},\\n\\tabstract = {In the past decades recommender systems have become a powerful tool to\\nimprove personalization on the Web. Yet, many popular websites lack such\\nfunctionality, its implementation usually requires certain technical\\nskills, and, above all, its introduction is beyond the scope and control\\nof end-users. To alleviate these problems, this paper presents a novel\\ntool to empower end-users without programming skills, without any\\ninvolvement of website providers, to embed personalized recommendations of\\nitems into arbitrary websites on client-side. For this we have developed a\\ngeneric meta-model to capture recommender system configuration parameters\\nin general as well as in a web augmentation context. Thereupon, we have\\nimplemented a wizard in the form of an easy-to-use browser plug-in,\\nallowing the generation of so-called user scripts, which are executed in\\nthe browser to engage collaborative filtering functionality from a\\nprovided external rest service. We discuss functionality and limitations\\nof the approach, and in a study with end-users we assess the usability and\\nshow its suitability for combining recommender systems with web\\naugmentation techniques, aiming to empower end-users to implement\\ncontrollable recommender applications for a more personalized browsing\\nexperience.},\\n\\tnumber = {5},\\n\\tjournal = {Multimed. Tools Appl.},\\n\\tauthor = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Bosetti, Gabriela and Kapsammer, Elisabeth},\\n\\tmonth = feb,\\n\\tyear = {2021},\\n\\tpages = {6785--6809},\\n}\\n\\n\",\"author_short\":[\"Wischenbart, M.\",\"Firmenich, S.\",\"Rossi, G.\",\"Bosetti, G.\",\"Kapsammer, E.\"],\"key\":\"wischenbart_engaging_2021\",\"id\":\"wischenbart_engaging_2021\",\"bibbaseid\":\"wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s11042-020-09803-8\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"unpublished\",\"type\":\"unpublished\",\"title\":\"Improving Accountability in Recommender Systems Research Through Reproducibility\",\"url\":\"http://arxiv.org/abs/2102.00482\",\"abstract\":\"Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving towards fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human-Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts, and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature, and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem, and facilitate progress in the field by increasing the accountability of research.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Bellogín\"],\"firstnames\":[\"Alejandro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Said\"],\"firstnames\":[\"Alan\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2021\",\"note\":\"ISBN: 2102.00482 Publication Title: arXiv [cs.IR]\",\"bibtex\":\"@unpublished{bellogin_improving_2021,\\n\\ttitle = {Improving {Accountability} in {Recommender} {Systems} {Research} {Through} {Reproducibility}},\\n\\turl = {http://arxiv.org/abs/2102.00482},\\n\\tabstract = {Reproducibility is a key requirement for scientific progress. It allows\\nthe reproduction of the works of others, and, as a consequence, to fully\\ntrust the reported claims and results. In this work, we argue that, by\\nfacilitating reproducibility of recommender systems experimentation, we\\nindirectly address the issues of accountability and transparency in\\nrecommender systems research from the perspectives of practitioners,\\ndesigners, and engineers aiming to assess the capabilities of published\\nresearch works. These issues have become increasingly prevalent in recent\\nliterature. Reasons for this include societal movements around intelligent\\nsystems and artificial intelligence striving towards fair and objective\\nuse of human behavioral data (as in Machine Learning, Information\\nRetrieval, or Human-Computer Interaction). Society has grown to expect\\nexplanations and transparency standards regarding the underlying\\nalgorithms making automated decisions for and around us. This work surveys\\nexisting definitions of these concepts, and proposes a coherent\\nterminology for recommender systems research, with the goal to connect\\nreproducibility to accountability. We achieve this by introducing several\\nguidelines and steps that lead to reproducible and, hence, accountable\\nexperimental workflows and research. We additionally analyze several\\ninstantiations of recommender system implementations available in the\\nliterature, and discuss the extent to which they fit in the introduced\\nframework. With this work, we aim to shed light on this important problem,\\nand facilitate progress in the field by increasing the accountability of\\nresearch.},\\n\\tauthor = {Bellogín, Alejandro and Said, Alan},\\n\\tmonth = jan,\\n\\tyear = {2021},\\n\\tnote = {ISBN: 2102.00482\\nPublication Title: arXiv [cs.IR]},\\n}\\n\\n\",\"author_short\":[\"Bellogín, A.\",\"Said, A.\"],\"key\":\"bellogin_improving_2021\",\"id\":\"bellogin_improving_2021\",\"bibbaseid\":\"bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://arxiv.org/abs/2102.00482\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems\",\"volume\":\"32\",\"issn\":\"1091-9856\",\"url\":\"https://doi.org/10.1287/ijoc.2018.0882\",\"doi\":\"10.1287/ijoc.2018.0882\",\"abstract\":\"In this study, we investigate how individual users? rating characteristics affect the user-level performance of recommendation algorithms. We measure users? rating characteristics from three perspectives: rating value, rating structure, and neighborhood network embeddedness. We study how these three categories of measures influence the predictive accuracy of popular recommendation algorithms for each user. Our experiments use five real-world data sets with varying characteristics. For each individual user, we estimate the predictive accuracy of three recommendation algorithms. We then apply regression-based models to uncover the relationships between rating characteristics and recommendation performance at the individual user level. Our experimental results show consistent and significant effects of several rating measures on recommendation accuracy. Understanding how rating characteristics affect the recommendation performance at the individual user level has practical implications for the design of recommender systems.\",\"number\":\"2\",\"journal\":\"INFORMS J. Comput.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Cheng\"],\"firstnames\":[\"Xiaoye\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Jingjing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yan\"],\"firstnames\":[\"Lu\",\"(lucy)\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2020\",\"note\":\"Publisher: INFORMS\",\"pages\":\"303–320\",\"bibtex\":\"@article{cheng_understanding_2020,\\n\\ttitle = {Understanding the {Impact} of {Individual} {Users}’ {Rating} {Characteristics} on the {Predictive} {Accuracy} of {Recommender} {Systems}},\\n\\tvolume = {32},\\n\\tissn = {1091-9856},\\n\\turl = {https://doi.org/10.1287/ijoc.2018.0882},\\n\\tdoi = {10.1287/ijoc.2018.0882},\\n\\tabstract = {In this study, we investigate how individual users? rating characteristics\\naffect the user-level performance of recommendation algorithms. We measure\\nusers? rating characteristics from three perspectives: rating value,\\nrating structure, and neighborhood network embeddedness. We study how\\nthese three categories of measures influence the predictive accuracy of\\npopular recommendation algorithms for each user. Our experiments use five\\nreal-world data sets with varying characteristics. For each individual\\nuser, we estimate the predictive accuracy of three recommendation\\nalgorithms. We then apply regression-based models to uncover the\\nrelationships between rating characteristics and recommendation\\nperformance at the individual user level. Our experimental results show\\nconsistent and significant effects of several rating measures on\\nrecommendation accuracy. Understanding how rating characteristics affect\\nthe recommendation performance at the individual user level has practical\\nimplications for the design of recommender systems.},\\n\\tnumber = {2},\\n\\tjournal = {INFORMS J. Comput.},\\n\\tauthor = {Cheng, Xiaoye and Zhang, Jingjing and Yan, Lu (lucy)},\\n\\tmonth = apr,\\n\\tyear = {2020},\\n\\tnote = {Publisher: INFORMS},\\n\\tpages = {303--320},\\n}\\n\\n\",\"author_short\":[\"Cheng, X.\",\"Zhang, J.\",\"Yan, L. (.\"],\"key\":\"cheng_understanding_2020\",\"id\":\"cheng_understanding_2020\",\"bibbaseid\":\"cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1287/ijoc.2018.0882\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"rrecsys: An R-package for Prototyping Recommendation Algorithms\",\"url\":\"https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf\",\"abstract\":\"ABSTRACT We introduce rrecsys , an open source extension package in R for rapid prototyping and intuitive assessment of recommender system algorithms. As the only currently available R package for recommender algorithms (recommenderlab) did not\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Çoba\"],\"firstnames\":[\"Ludovik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zanker\"],\"firstnames\":[\"Markus\"],\"suffixes\":[]}],\"year\":\"2016\",\"keywords\":\"toolkit\",\"bibtex\":\"@inproceedings{coba_rrecsys_2016,\\n\\ttitle = {rrecsys: {An} {R}-package for {Prototyping} {Recommendation} {Algorithms}},\\n\\turl = {https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf},\\n\\tabstract = {ABSTRACT We introduce rrecsys , an open source extension package in R for\\nrapid prototyping and intuitive assessment of recommender system\\nalgorithms. As the only currently available R package for recommender\\nalgorithms (recommenderlab) did not},\\n\\tauthor = {Çoba, Ludovik and Zanker, Markus},\\n\\tyear = {2016},\\n\\tkeywords = {toolkit},\\n}\\n\\n\",\"author_short\":[\"Çoba, L.\",\"Zanker, M.\"],\"key\":\"coba_rrecsys_2016\",\"id\":\"coba_rrecsys_2016\",\"bibbaseid\":\"oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf\"},\"keyword\":[\"toolkit\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"title\":\"A Multi-objective Autotuning Framework For The Java Virtual Machine\",\"url\":\"https://digital.library.txstate.edu/handle/10877/6096\",\"abstract\":\"Due to inherent limitations in performance, Java was not considered a suitable platform for for scalable high-performance computing (HPC) for a long time. The scenario is changing because of the development of frameworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in usage, achieving high performance with Java is not trivial. High performance in Java relies on libraries providing explicit threads or relying on runnable-like interfaces for distributed programming. In this thesis, we develop an autotuning framework for JVM that manages multiple objective functions including execution time, power consumption, energy and perfomance-per-watt. The framework searches the combined space of JIT optimization sequences and different classes of JVM runtime parameters. To discover good configurations more quickly, the framework implements novel heuristic search algorithms. To reduce the size of the search space machine-learning based pruning techniques are used. Evaluation on recommender system workloads show that significant improvements in both performance and power can be gained by fine-tuning JVM runitme parameters.\",\"urldate\":\"2016-07-05\",\"school\":\"Texas State University\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Saha\"],\"firstnames\":[\"Shuvabrata\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2016\",\"bibtex\":\"@phdthesis{saha_multi-objective_2016,\\n\\ttitle = {A {Multi}-objective {Autotuning} {Framework} {For} {The} {Java} {Virtual} {Machine}},\\n\\turl = {https://digital.library.txstate.edu/handle/10877/6096},\\n\\tabstract = {Due to inherent limitations in performance, Java was not considered a\\nsuitable platform for for scalable high-performance computing (HPC) for a\\nlong time. The scenario is changing because of the development of\\nframeworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in\\nusage, achieving high performance with Java is not trivial. High\\nperformance in Java relies on libraries providing explicit threads or\\nrelying on runnable-like interfaces for distributed programming. In this\\nthesis, we develop an autotuning framework for JVM that manages multiple\\nobjective functions including execution time, power consumption, energy\\nand perfomance-per-watt. The framework searches the combined space of JIT\\noptimization sequences and different classes of JVM runtime parameters. To\\ndiscover good configurations more quickly, the framework implements novel\\nheuristic search algorithms. To reduce the size of the search space\\nmachine-learning based pruning techniques are used. Evaluation on\\nrecommender system workloads show that significant improvements in both\\nperformance and power can be gained by fine-tuning JVM runitme parameters.},\\n\\turldate = {2016-07-05},\\n\\tschool = {Texas State University},\\n\\tauthor = {Saha, Shuvabrata},\\n\\tmonth = apr,\\n\\tyear = {2016},\\n}\\n\\n\",\"author_short\":[\"Saha, S.\"],\"key\":\"saha_multi-objective_2016\",\"id\":\"saha_multi-objective_2016\",\"bibbaseid\":\"saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://digital.library.txstate.edu/handle/10877/6096\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Personalized Micro-Service Recommendation System for Online News\",\"volume\":\"160\",\"issn\":\"1877-0509\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S1877050919317399\",\"doi\":\"10.1016/j.procs.2019.11.039\",\"abstract\":\"In the era of artificial intelligence and high technology advance our life is dependent on them in every aspect. The dynamic environment forces us to plan our time with conscious and every minute is valuable. To help individuals and corporations see information that is only relevant to them, recommendation systems are in place. Popular platforms that such as Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation systems to better serve the needed of their users. This research paper gives insight of building a microservice recommendation system for online news. Research in recommendation systems is mainly focused on improving user’s experience based mainly on personalization information, such as preferences, and searching history. To determine the initial preferences of a user an initial menu of topics/themes is provided for the user to choose from. In order to reflect as precise as possible the searching interests regarding news of user, all of his interactions are thoroughly recorded and in depth analyzed, based on advanced machine learning techniques, when adjusting the news topics, the user is interested for. Based on the aforementioned approach, a personalized recommendation system for online news has been developed. Existing techniques has been researched and evaluated to aid the decision about picking the best approach for the software to be implemented. Frameworks/technologies used for the development are Java 8, Spring boot, Spring MVC, Maven and MongoDB.\",\"journal\":\"Procedia Comput. Sci.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Asenova\"],\"firstnames\":[\"Marchela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chrysoulas\"],\"firstnames\":[\"Christos\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2019\",\"keywords\":\"TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases\",\"pages\":\"610–615\",\"bibtex\":\"@article{asenova_personalized_2019,\\n\\ttitle = {Personalized {Micro}-{Service} {Recommendation} {System} for {Online} {News}},\\n\\tvolume = {160},\\n\\tissn = {1877-0509},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S1877050919317399},\\n\\tdoi = {10.1016/j.procs.2019.11.039},\\n\\tabstract = {In the era of artificial intelligence and high technology advance our life\\nis dependent on them in every aspect. The dynamic environment forces us to\\nplan our time with conscious and every minute is valuable. To help\\nindividuals and corporations see information that is only relevant to\\nthem, recommendation systems are in place. Popular platforms that such as\\nAmazon, Ebay, Netflix, YouTube, make use of advanced recommendation\\nsystems to better serve the needed of their users. This research paper\\ngives insight of building a microservice recommendation system for online\\nnews. Research in recommendation systems is mainly focused on improving\\nuser’s experience based mainly on personalization information, such as\\npreferences, and searching history. To determine the initial preferences\\nof a user an initial menu of topics/themes is provided for the user to\\nchoose from. In order to reflect as precise as possible the searching\\ninterests regarding news of user, all of his interactions are thoroughly\\nrecorded and in depth analyzed, based on advanced machine learning\\ntechniques, when adjusting the news topics, the user is interested for.\\nBased on the aforementioned approach, a personalized recommendation system\\nfor online news has been developed. Existing techniques has been\\nresearched and evaluated to aid the decision about picking the best\\napproach for the software to be implemented. Frameworks/technologies used\\nfor the development are Java 8, Spring boot, Spring MVC, Maven and\\nMongoDB.},\\n\\tjournal = {Procedia Comput. Sci.},\\n\\tauthor = {Asenova, Marchela and Chrysoulas, Christos},\\n\\tmonth = jan,\\n\\tyear = {2019},\\n\\tkeywords = {TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases},\\n\\tpages = {610--615},\\n}\\n\\n\",\"author_short\":[\"Asenova, M.\",\"Chrysoulas, C.\"],\"key\":\"asenova_personalized_2019\",\"id\":\"asenova_personalized_2019\",\"bibbaseid\":\"asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S1877050919317399\"},\"keyword\":[\"TF-IDF\",\"collaborative filtering\",\"cosine similarity\",\"recommendation engine\",\"recommendation phases\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm\",\"volume\":\"102\",\"issn\":\"0144-3097\",\"url\":\"http://link.springer.com/10.1007/s00607-018-0687-5\",\"doi\":\"10.1007/s00607-018-0687-5\",\"abstract\":\"Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.\",\"number\":\"2\",\"journal\":\"Computing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kotkov\"],\"firstnames\":[\"Denis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Veijalainen\"],\"firstnames\":[\"Jari\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Shuaiqiang\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2020\",\"pages\":\"393–411\",\"bibtex\":\"@article{kotkov_how_2020,\\n\\ttitle = {How does serendipity affect diversity in recommender systems? {A} serendipity-oriented greedy algorithm},\\n\\tvolume = {102},\\n\\tissn = {0144-3097},\\n\\turl = {http://link.springer.com/10.1007/s00607-018-0687-5},\\n\\tdoi = {10.1007/s00607-018-0687-5},\\n\\tabstract = {Most recommender systems suggest items that are popular among all users\\nand similar to items a user usually consumes. As a result, the user\\nreceives recommendations that she/he is already familiar with or would\\nfind anyway, leading to low satisfaction. To overcome this problem, a\\nrecommender system should suggest novel, relevant and unexpected i.e.,\\nserendipitous items. In this paper, we propose a serendipity-oriented,\\nreranking algorithm called a serendipity-oriented greedy (SOG) algorithm,\\nwhich improves serendipity of recommendations through feature\\ndiversification and helps overcome the overspecialization problem. To\\nevaluate our algorithm, we employed the only publicly available dataset\\ncontaining user feedback regarding serendipity. We compared our SOG\\nalgorithm with topic diversification, popularity baseline, singular value\\ndecomposition, serendipitous personalized ranking and Zheng’s algorithms\\nrelying on the above dataset. SOG outperforms other algorithms in terms of\\nserendipity and diversity. It also outperforms serendipity-oriented\\nalgorithms in terms of accuracy, but underperforms accuracy-oriented\\nalgorithms in terms of accuracy. We found that the increase of diversity\\ncan hurt accuracy and harm or improve serendipity depending on the size of\\ndiversity increase.},\\n\\tnumber = {2},\\n\\tjournal = {Computing},\\n\\tauthor = {Kotkov, Denis and Veijalainen, Jari and Wang, Shuaiqiang},\\n\\tmonth = feb,\\n\\tyear = {2020},\\n\\tpages = {393--411},\\n}\\n\\n\",\"author_short\":[\"Kotkov, D.\",\"Veijalainen, J.\",\"Wang, S.\"],\"key\":\"kotkov_how_2020\",\"id\":\"kotkov_how_2020\",\"bibbaseid\":\"kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com/10.1007/s00607-018-0687-5\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Evaluating Recommender System Stability with Influence-Guided Fuzzing\",\"url\":\"https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf\",\"abstract\":\"Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender's stability. We implement our approach …\",\"publisher\":\"AAAI\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shriver\"],\"firstnames\":[\"David\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Elbaum\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dwyer\"],\"firstnames\":[\"Matthew\",\"B\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rosenblum\"],\"firstnames\":[\"David\",\"S\"],\"suffixes\":[]}],\"year\":\"2019\",\"bibtex\":\"@inproceedings{shriver_evaluating_2019,\\n\\ttitle = {Evaluating {Recommender} {System} {Stability} with {Influence}-{Guided} {Fuzzing}},\\n\\turl = {https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf},\\n\\tabstract = {Recommender systems help users to find products or services they may like\\nwhen lacking personal experience or facing an overwhelming set of choices.\\nSince unstable recommendations can lead to distrust, loss of profits, and\\na poor user experience, it is important to test recommender system\\nstability. In this work, we present an approach based on inferred models\\nof influence that underlie recommender systems to guide the generation of\\ndataset modifications to assess a recommender's stability. We implement\\nour approach …},\\n\\tpublisher = {AAAI},\\n\\tauthor = {Shriver, David and Elbaum, Sebastian and Dwyer, Matthew B and Rosenblum, David S},\\n\\tyear = {2019},\\n}\\n\\n\",\"author_short\":[\"Shriver, D.\",\"Elbaum, S.\",\"Dwyer, M. B\",\"Rosenblum, D. S\"],\"key\":\"shriver_evaluating_2019\",\"id\":\"shriver_evaluating_2019\",\"bibbaseid\":\"shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"User preferences modeling using dirichlet process mixture model for a content-based recommender system\",\"issn\":\"0950-7051\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0950705118304799\",\"doi\":\"10.1016/j.knosys.2018.09.028\",\"abstract\":\"Recommender systems have been developed to assist users in retrieving relevant resources. Collaborative and content-based filtering are two basic approaches that are used in recommender systems. The former employs the feedback of users with similar interests, while the latter is based on the feature of the selected resources by each user. Recommender systems can consider users’ behavior to more accurately estimate their preferences via a list of recommendations. However, the existing approaches rarely consider both interests and preferences of the users. Also, the dynamic nature of user behavior poses an additional challenge for recommender systems. In this paper, we consider the interactions of each individual user, and analyze them to propose a user model and capture user’s interests. We construct the user model based on a Bayesian nonparametric framework, called the Dirichlet Process Mixture Model. The proposed model evolves following the dynamic nature of user behavior to adapt both the user interests and preferences. We implemented the proposed model and evaluated it using both the MovieLens dataset, and a real-world dataset that contains news tweets from five news channels (New York Times, BBC, CNN, Reuters and Associated Press). The experimental results and comparisons with several recently developed approaches show the superiority in accuracy of the proposed approach, and its ability to adapt with user behavior over time.\",\"journal\":\"Knowledge-Based Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Cami\"],\"firstnames\":[\"Bagher\",\"Rahimpour\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hassanpour\"],\"firstnames\":[\"Hamid\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mashayekhi\"],\"firstnames\":[\"Hoda\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2018\",\"keywords\":\"Temporal content-based recommender systems, User behavior modeling, User preferences modeling\",\"bibtex\":\"@article{cami_user_2018,\\n\\ttitle = {User preferences modeling using dirichlet process mixture model for a content-based recommender system},\\n\\tissn = {0950-7051},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S0950705118304799},\\n\\tdoi = {10.1016/j.knosys.2018.09.028},\\n\\tabstract = {Recommender systems have been developed to assist users in retrieving\\nrelevant resources. Collaborative and content-based filtering are two\\nbasic approaches that are used in recommender systems. The former employs\\nthe feedback of users with similar interests, while the latter is based on\\nthe feature of the selected resources by each user. Recommender systems\\ncan consider users’ behavior to more accurately estimate their preferences\\nvia a list of recommendations. However, the existing approaches rarely\\nconsider both interests and preferences of the users. Also, the dynamic\\nnature of user behavior poses an additional challenge for recommender\\nsystems. In this paper, we consider the interactions of each individual\\nuser, and analyze them to propose a user model and capture user’s\\ninterests. We construct the user model based on a Bayesian nonparametric\\nframework, called the Dirichlet Process Mixture Model. The proposed model\\nevolves following the dynamic nature of user behavior to adapt both the\\nuser interests and preferences. We implemented the proposed model and\\nevaluated it using both the MovieLens dataset, and a real-world dataset\\nthat contains news tweets from five news channels (New York Times, BBC,\\nCNN, Reuters and Associated Press). The experimental results and\\ncomparisons with several recently developed approaches show the\\nsuperiority in accuracy of the proposed approach, and its ability to adapt\\nwith user behavior over time.},\\n\\tjournal = {Knowledge-Based Systems},\\n\\tauthor = {Cami, Bagher Rahimpour and Hassanpour, Hamid and Mashayekhi, Hoda},\\n\\tmonth = sep,\\n\\tyear = {2018},\\n\\tkeywords = {Temporal content-based recommender systems, User behavior modeling, User preferences modeling},\\n}\\n\\n\",\"author_short\":[\"Cami, B. R.\",\"Hassanpour, H.\",\"Mashayekhi, H.\"],\"key\":\"cami_user_2018\",\"id\":\"cami_user_2018\",\"bibbaseid\":\"cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0950705118304799\"},\"keyword\":[\"Temporal content-based recommender systems\",\"User behavior modeling\",\"User preferences modeling\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"FAiR: A Framework for Analyses and Evaluations on Recommender Systems\",\"url\":\"http://dx.doi.org/10.1007/978-3-319-95168-3_26\",\"doi\":\"10.1007/978-3-319-95168-3_26\",\"abstract\":\"Recommender systems (RSs) have become essential tools in e-commerce applications, helping users in the decision-making process. Evaluation on these tools is, however, a major divergence point nowadays, since there is no consensus regarding which metrics are necessary to consolidate new RSs. For this reason, distinct frameworks have been developed to ease the deployment of RSs in research and/or production environments. In the present work, we perform an extensive study of the most popular evaluation metrics, organizing them into three groups: Effectiveness-based, Complementary Dimensions of Quality and Domain Profiling. Further, we consolidate a framework named FAiR to help researchers in evaluating their RSs using these metrics, besides identifying the characteristics of data collections that may intrinsically affect RSs performance. FAiR is compatible with the output format of the main existing RSs libraries (i.e., MyMediaLite and LensKit).\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Carvalho\"],\"firstnames\":[\"Diego\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Silva\"],\"firstnames\":[\"Nícollas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Silveira\"],\"firstnames\":[\"Thiago\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mourão\"],\"firstnames\":[\"Fernando\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pereira\"],\"firstnames\":[\"Adriano\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dias\"],\"firstnames\":[\"Diego\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rocha\"],\"firstnames\":[\"Leonardo\"],\"suffixes\":[]}],\"year\":\"2018\",\"pages\":\"383–397\",\"bibtex\":\"@inproceedings{carvalho_fair_2018,\\n\\ttitle = {{FAiR}: {A} {Framework} for {Analyses} and {Evaluations} on {Recommender} {Systems}},\\n\\turl = {http://dx.doi.org/10.1007/978-3-319-95168-3_26},\\n\\tdoi = {10.1007/978-3-319-95168-3_26},\\n\\tabstract = {Recommender systems (RSs) have become essential tools in e-commerce\\napplications, helping users in the decision-making process. Evaluation on\\nthese tools is, however, a major divergence point nowadays, since there is\\nno consensus regarding which metrics are necessary to consolidate new RSs.\\nFor this reason, distinct frameworks have been developed to ease the\\ndeployment of RSs in research and/or production environments. In the\\npresent work, we perform an extensive study of the most popular evaluation\\nmetrics, organizing them into three groups: Effectiveness-based,\\nComplementary Dimensions of Quality and Domain Profiling. Further, we\\nconsolidate a framework named FAiR to help researchers in evaluating their\\nRSs using these metrics, besides identifying the characteristics of data\\ncollections that may intrinsically affect RSs performance. FAiR is\\ncompatible with the output format of the main existing RSs libraries\\n(i.e., MyMediaLite and LensKit).},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Carvalho, Diego and Silva, Nícollas and Silveira, Thiago and Mourão, Fernando and Pereira, Adriano and Dias, Diego and Rocha, Leonardo},\\n\\tyear = {2018},\\n\\tpages = {383--397},\\n}\\n\\n\",\"author_short\":[\"Carvalho, D.\",\"Silva, N.\",\"Silveira, T.\",\"Mourão, F.\",\"Pereira, A.\",\"Dias, D.\",\"Rocha, L.\"],\"key\":\"carvalho_fair_2018\",\"id\":\"carvalho_fair_2018\",\"bibbaseid\":\"carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1007/978-3-319-95168-3_26\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Replicating and Improving Top-N Recommendations in Open Source Packages\",\"url\":\"http://doi.acm.org/10.1145/3227609.3227671\",\"doi\":\"10.1145/3227609.3227671\",\"booktitle\":\"WIMS '18\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Coba\"],\"firstnames\":[\"Ludovik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Symeonidis\"],\"firstnames\":[\"Panagiotis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zanker\"],\"firstnames\":[\"Markus\"],\"suffixes\":[]}],\"year\":\"2018\",\"note\":\"Journal Abbreviation: WIMS '18\",\"keywords\":\"Collaborative Filtering, Recommendation algorithms, evaluation\",\"pages\":\"40:1–40:7\",\"bibtex\":\"@inproceedings{coba_replicating_2018,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Replicating and {Improving} {Top}-{N} {Recommendations} in {Open} {Source} {Packages}},\\n\\turl = {http://doi.acm.org/10.1145/3227609.3227671},\\n\\tdoi = {10.1145/3227609.3227671},\\n\\tbooktitle = {{WIMS} '18},\\n\\tpublisher = {ACM},\\n\\tauthor = {Coba, Ludovik and Symeonidis, Panagiotis and Zanker, Markus},\\n\\tyear = {2018},\\n\\tnote = {Journal Abbreviation: WIMS '18},\\n\\tkeywords = {Collaborative Filtering, Recommendation algorithms, evaluation},\\n\\tpages = {40:1--40:7},\\n}\\n\\n\",\"author_short\":[\"Coba, L.\",\"Symeonidis, P.\",\"Zanker, M.\"],\"key\":\"coba_replicating_2018\",\"id\":\"coba_replicating_2018\",\"bibbaseid\":\"coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/3227609.3227671\"},\"keyword\":[\"Collaborative Filtering\",\"Recommendation algorithms\",\"evaluation\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm\",\"volume\":\"20\",\"issn\":\"1520-9210\",\"url\":\"http://dx.doi.org/10.1109/TMM.2017.2779043\",\"doi\":\"10.1109/TMM.2017.2779043\",\"abstract\":\"In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N recommendation with collaborative filtering (CF). Existing studies commonly use rated items (which form only a small section in a rating matrix) or import some additional information (e.g., details about the items and users) to improve the performance of CF. Unlike these methods, we propose a novel notion towards a huge amount of unrated items: serendipity item. By utilizing serendipity items, we propose concise satisfaction and interest injection (CSII), a method that can effectively find interesting, satisfying, and serendipitous items in unrated items. By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially. Meanwhile, it can address the sparsity and cold-start issues by enriching the rating matrix in CF without additional information. As our method tackles rating matrix before recommendation procedure, it can be applied to most existing CF methods, such as item-based CF, user-based CF and matrix factorization-based CF. Through comprehensive experiments using abundant real-world datasets with LensKit implementation, we successfully demonstrate that our solution improves the performance of existing CF methods consistently and universally. Moreover, comparing with baseline methods, CSII can extract uninteresting items more carefully and cautiously, avoiding potential items inferred by mistake.\",\"number\":\"7\",\"journal\":\"IEEE Trans. Multimedia\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Y\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xu\"],\"firstnames\":[\"Y\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"E\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Han\"],\"firstnames\":[\"J\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yu\"],\"firstnames\":[\"Z\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2018\",\"keywords\":\"CF methods, CSII, Collaboration, Collaborative filtering, Computer science, Data mining, Lifting equipment, Multimedia communication, Recommender systems, cold-start issues, collaborative filtering, collaborative filtering recommendations, concise satisfaction and interest injection, item-based CF, matrix decomposition, matrix factorization, matrix factorization-based CF, rating matrix, recommendation quality, recommender systems, serendipitous recommendation, serendipity item, top-N items, top-N recommendation, unrated items, user-based CF\",\"pages\":\"1888–1900\",\"bibtex\":\"@article{yang_improving_2018,\\n\\ttitle = {Improving {Existing} {Collaborative} {Filtering} {Recommendations} via {Serendipity}-{Based} {Algorithm}},\\n\\tvolume = {20},\\n\\tissn = {1520-9210},\\n\\turl = {http://dx.doi.org/10.1109/TMM.2017.2779043},\\n\\tdoi = {10.1109/TMM.2017.2779043},\\n\\tabstract = {In this paper, we study how to address the sparsity, accuracy and\\nserendipity issues of top-N recommendation with collaborative filtering\\n(CF). Existing studies commonly use rated items (which form only a small\\nsection in a rating matrix) or import some additional information (e.g.,\\ndetails about the items and users) to improve the performance of CF.\\nUnlike these methods, we propose a novel notion towards a huge amount of\\nunrated items: serendipity item. By utilizing serendipity items, we\\npropose concise satisfaction and interest injection (CSII), a method that\\ncan effectively find interesting, satisfying, and serendipitous items in\\nunrated items. By preventing uninteresting and unsatisfying items to be\\nrecommended as top-N items, this concise-but-novel method improves\\naccuracy and recommendation quality (especially serendipity)\\nsubstantially. Meanwhile, it can address the sparsity and cold-start\\nissues by enriching the rating matrix in CF without additional\\ninformation. As our method tackles rating matrix before recommendation\\nprocedure, it can be applied to most existing CF methods, such as\\nitem-based CF, user-based CF and matrix factorization-based CF. Through\\ncomprehensive experiments using abundant real-world datasets with LensKit\\nimplementation, we successfully demonstrate that our solution improves the\\nperformance of existing CF methods consistently and universally. Moreover,\\ncomparing with baseline methods, CSII can extract uninteresting items more\\ncarefully and cautiously, avoiding potential items inferred by mistake.},\\n\\tnumber = {7},\\n\\tjournal = {IEEE Trans. Multimedia},\\n\\tauthor = {Yang, Y and Xu, Y and Wang, E and Han, J and Yu, Z},\\n\\tmonth = jul,\\n\\tyear = {2018},\\n\\tkeywords = {CF methods, CSII, Collaboration, Collaborative filtering, Computer science, Data mining, Lifting equipment, Multimedia communication, Recommender systems, cold-start issues, collaborative filtering, collaborative filtering recommendations, concise satisfaction and interest injection, item-based CF, matrix decomposition, matrix factorization, matrix factorization-based CF, rating matrix, recommendation quality, recommender systems, serendipitous recommendation, serendipity item, top-N items, top-N recommendation, unrated items, user-based CF},\\n\\tpages = {1888--1900},\\n}\\n\\n\",\"author_short\":[\"Yang, Y\",\"Xu, Y\",\"Wang, E\",\"Han, J\",\"Yu, Z\"],\"key\":\"yang_improving_2018\",\"id\":\"yang_improving_2018\",\"bibbaseid\":\"yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1109/TMM.2017.2779043\"},\"keyword\":[\"CF methods\",\"CSII\",\"Collaboration\",\"Collaborative filtering\",\"Computer science\",\"Data mining\",\"Lifting equipment\",\"Multimedia communication\",\"Recommender systems\",\"cold-start issues\",\"collaborative filtering\",\"collaborative filtering recommendations\",\"concise satisfaction and interest injection\",\"item-based CF\",\"matrix decomposition\",\"matrix factorization\",\"matrix factorization-based CF\",\"rating matrix\",\"recommendation quality\",\"recommender systems\",\"serendipitous recommendation\",\"serendipity item\",\"top-N items\",\"top-N recommendation\",\"unrated items\",\"user-based CF\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"mastersthesis\",\"type\":\"mastersthesis\",\"title\":\"Assessing the Quality and Stability of Recommender Systems\",\"url\":\"https://digitalcommons.unl.edu/computerscidiss/147\",\"abstract\":\"Recommender systems help users to find products they may like when lacking personal experience or facing an overwhelmingly large set of items. However, assessing the quality and stability of recommender systems can present challenges for developers. First, traditional accuracy metrics, such as precision and recall, for validating the quality of recommendations, offer only a coarse, one-dimensional view of the system performance. Second, assessing the stability of a recommender systems requires generating new data and retraining a system, which is expensive. In this work, we present two new approaches for assessing the quality and stability of recommender systems to address these challenges. We first present a general and extensible approach for assessing the quality of the behavior of a recommender system using logical property templates. The approach is general in that it defines recommendation systems in terms of sets of rankings, ratings, users, and items on which property templates are defined. It is extensible in that these property templates define a space of properties that can be instantiated and parameterized to characterize a recommendation system. We study the application of the approach to several recommendation systems. Our findings demonstrate the potential of these properties, illustrating the insights they can provide about the different algorithms and evolving datasets. We also present an approach for influence-guided fuzz testing of recommender system stability. We infer influence models for aspects of a dataset, such as users or items, from the recommendations produced by a recommender system and its training data. We define dataset fuzzing heuristics that use these influence models for generating modifications to an original dataset and we present a test oracle based on a threshold of acceptable instability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset and we find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches. Adviser: Sebastian Elbaum\",\"urldate\":\"2018-05-08\",\"school\":\"University of Nebraska - Lincoln\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shriver\"],\"firstnames\":[\"David\"],\"suffixes\":[]}],\"collaborator\":\"Elbaum, Sebastian\",\"year\":\"2018\",\"note\":\"Publication Title: Computer Science and Engineering\",\"bibtex\":\"@mastersthesis{shriver_assessing_2018,\\n\\ttitle = {Assessing the {Quality} and {Stability} of {Recommender} {Systems}},\\n\\turl = {https://digitalcommons.unl.edu/computerscidiss/147},\\n\\tabstract = {Recommender systems help users to find products they may like when lacking\\npersonal experience or facing an overwhelmingly large set of items.\\nHowever, assessing the quality and stability of recommender systems can\\npresent challenges for developers. First, traditional accuracy metrics,\\nsuch as precision and recall, for validating the quality of\\nrecommendations, offer only a coarse, one-dimensional view of the system\\nperformance. Second, assessing the stability of a recommender systems\\nrequires generating new data and retraining a system, which is expensive.\\nIn this work, we present two new approaches for assessing the quality and\\nstability of recommender systems to address these challenges. We first\\npresent a general and extensible approach for assessing the quality of the\\nbehavior of a recommender system using logical property templates. The\\napproach is general in that it defines recommendation systems in terms of\\nsets of rankings, ratings, users, and items on which property templates\\nare defined. It is extensible in that these property templates define a\\nspace of properties that can be instantiated and parameterized to\\ncharacterize a recommendation system. We study the application of the\\napproach to several recommendation systems. Our findings demonstrate the\\npotential of these properties, illustrating the insights they can provide\\nabout the different algorithms and evolving datasets. We also present an\\napproach for influence-guided fuzz testing of recommender system\\nstability. We infer influence models for aspects of a dataset, such as\\nusers or items, from the recommendations produced by a recommender system\\nand its training data. We define dataset fuzzing heuristics that use these\\ninfluence models for generating modifications to an original dataset and\\nwe present a test oracle based on a threshold of acceptable instability.\\nWe implement our approach and evaluate it on several recommender\\nalgorithms using the MovieLens dataset and we find that influence-guided\\nfuzzing can effectively find small sets of modifications that cause\\nsignificantly more instability than random approaches. Adviser: Sebastian\\nElbaum},\\n\\turldate = {2018-05-08},\\n\\tschool = {University of Nebraska - Lincoln},\\n\\tauthor = {Shriver, David},\\n\\tcollaborator = {Elbaum, Sebastian},\\n\\tyear = {2018},\\n\\tnote = {Publication Title: Computer Science and Engineering},\\n}\\n\\n\",\"author_short\":[\"Shriver, D.\"],\"key\":\"shriver_assessing_2018\",\"id\":\"shriver_assessing_2018\",\"bibbaseid\":\"shriver-assessingthequalityandstabilityofrecommendersystems-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://digitalcommons.unl.edu/computerscidiss/147\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Evaluating Item-Item Similarity Algorithms for Movies\",\"url\":\"http://doi.acm.org/10.1145/2851581.2892362\",\"doi\":\"10.1145/2851581.2892362\",\"booktitle\":\"CHI EA '16\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Colucci\"],\"firstnames\":[\"Lucas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Doshi\"],\"firstnames\":[\"Prachi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lee\"],\"firstnames\":[\"Kun-Lin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liang\"],\"firstnames\":[\"Jiajie\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lin\"],\"firstnames\":[\"Yin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vashishtha\"],\"firstnames\":[\"Ishan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Jia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jude\"],\"firstnames\":[\"Alvin\"],\"suffixes\":[]}],\"year\":\"2016\",\"note\":\"Journal Abbreviation: CHI EA '16\",\"keywords\":\"algorithm evaluation, item-item similarity, recommender systems\",\"pages\":\"2141–2147\",\"bibtex\":\"@inproceedings{colucci_evaluating_2016,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Evaluating {Item}-{Item} {Similarity} {Algorithms} for {Movies}},\\n\\turl = {http://doi.acm.org/10.1145/2851581.2892362},\\n\\tdoi = {10.1145/2851581.2892362},\\n\\tbooktitle = {{CHI} {EA} '16},\\n\\tpublisher = {ACM},\\n\\tauthor = {Colucci, Lucas and Doshi, Prachi and Lee, Kun-Lin and Liang, Jiajie and Lin, Yin and Vashishtha, Ishan and Zhang, Jia and Jude, Alvin},\\n\\tyear = {2016},\\n\\tnote = {Journal Abbreviation: CHI EA '16},\\n\\tkeywords = {algorithm evaluation, item-item similarity, recommender systems},\\n\\tpages = {2141--2147},\\n}\\n\\n\",\"author_short\":[\"Colucci, L.\",\"Doshi, P.\",\"Lee, K.\",\"Liang, J.\",\"Lin, Y.\",\"Vashishtha, I.\",\"Zhang, J.\",\"Jude, A.\"],\"key\":\"colucci_evaluating_2016\",\"id\":\"colucci_evaluating_2016\",\"bibbaseid\":\"colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2851581.2892362\"},\"keyword\":[\"algorithm evaluation\",\"item-item similarity\",\"recommender systems\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks\",\"url\":\"http://dx.doi.org/10.1145/2645710.2645746\",\"doi\":\"10.1145/2645710.2645746\",\"abstract\":\"Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from different recommender systems due to the many options in design and implementation of an evaluation strategy. Additionally, algorithmic implementations can diverge from the standard formulation due to manual tuning and modifications that work better in some situations. In this work we compare common recommendation algorithms as implemented in three popular recommendation frameworks. To provide a fair comparison, we have complete control of the evaluation dimensions being benchmarked: dataset, data splitting, evaluation strategies, and metrics. We also include results using the internal evaluation mechanisms of these frameworks. Our analysis points to large differences in recommendation accuracy across frameworks and strategies, i.e. the same baselines may perform orders of magnitude better or worse across frameworks. Our results show the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results.\",\"urldate\":\"2017-02-03\",\"booktitle\":\"RecSys '14\",\"publisher\":\"ACM Press\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Said\"],\"firstnames\":[\"Alan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bellogin\"],\"firstnames\":[\"Alejandro\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2014\",\"note\":\"Journal Abbreviation: RecSys '14\",\"keywords\":\"toolkit\",\"pages\":\"129–136\",\"bibtex\":\"@inproceedings{said_comparative_2014,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Comparative {Recommender} {System} {Evaluation}: {Benchmarking} {Recommendation} {Frameworks}},\\n\\turl = {http://dx.doi.org/10.1145/2645710.2645746},\\n\\tdoi = {10.1145/2645710.2645746},\\n\\tabstract = {Recommender systems research is often based on comparisons of predictive\\naccuracy: the better the evaluation scores, the better the recommender.\\nHowever, it is difficult to compare results from different recommender\\nsystems due to the many options in design and implementation of an\\nevaluation strategy. Additionally, algorithmic implementations can diverge\\nfrom the standard formulation due to manual tuning and modifications that\\nwork better in some situations. In this work we compare common\\nrecommendation algorithms as implemented in three popular recommendation\\nframeworks. To provide a fair comparison, we have complete control of the\\nevaluation dimensions being benchmarked: dataset, data splitting,\\nevaluation strategies, and metrics. We also include results using the\\ninternal evaluation mechanisms of these frameworks. Our analysis points to\\nlarge differences in recommendation accuracy across frameworks and\\nstrategies, i.e. the same baselines may perform orders of magnitude better\\nor worse across frameworks. Our results show the necessity of clear\\nguidelines when reporting evaluation of recommender systems to ensure\\nreproducibility and comparison of results.},\\n\\turldate = {2017-02-03},\\n\\tbooktitle = {{RecSys} '14},\\n\\tpublisher = {ACM Press},\\n\\tauthor = {Said, Alan and Bellogin, Alejandro},\\n\\tmonth = oct,\\n\\tyear = {2014},\\n\\tnote = {Journal Abbreviation: RecSys '14},\\n\\tkeywords = {toolkit},\\n\\tpages = {129--136},\\n}\\n\\n\",\"author_short\":[\"Said, A.\",\"Bellogin, A.\"],\"key\":\"said_comparative_2014\",\"id\":\"said_comparative_2014\",\"bibbaseid\":\"said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1145/2645710.2645746\"},\"keyword\":[\"toolkit\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Putting Users in Control of Their Recommendations\",\"url\":\"http://doi.acm.org/10.1145/2792838.2800179\",\"doi\":\"10.1145/2792838.2800179\",\"abstract\":\"The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like \\\"show more popular items\\\". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.\",\"urldate\":\"2015-09-19\",\"booktitle\":\"RecSys '15\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Harper\"],\"firstnames\":[\"F\",\"Maxwell\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xu\"],\"firstnames\":[\"Funing\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kaur\"],\"firstnames\":[\"Harmanpreet\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Condiff\"],\"firstnames\":[\"Kyle\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chang\"],\"firstnames\":[\"Shuo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Terveen\"],\"firstnames\":[\"Loren\"],\"suffixes\":[]}],\"year\":\"2015\",\"note\":\"Journal Abbreviation: RecSys '15\",\"pages\":\"3–10\",\"bibtex\":\"@inproceedings{harper_putting_2015,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Putting {Users} in {Control} of {Their} {Recommendations}},\\n\\turl = {http://doi.acm.org/10.1145/2792838.2800179},\\n\\tdoi = {10.1145/2792838.2800179},\\n\\tabstract = {The essence of a recommender system is that it can recommend items\\npersonalized to the preferences of an individual user. But typically users\\nare given no explicit control over this personalization, and are instead\\nleft guessing about how their actions affect the resulting\\nrecommendations. We hypothesize that any recommender algorithm will better\\nfit some users' expectations than others, leaving opportunities for\\nimprovement. To address this challenge, we study a recommender that puts\\nsome control in the hands of users. Specifically, we build and evaluate a\\nsystem that incorporates user-tuned popularity and recency modifiers,\\nallowing users to express concepts like \\\"show more popular items\\\". We find\\nthat users who are given these controls evaluate the resulting\\nrecommendations much more positively. Further, we find that users diverge\\nin their preferred settings, confirming the importance of giving control\\nto users.},\\n\\turldate = {2015-09-19},\\n\\tbooktitle = {{RecSys} '15},\\n\\tpublisher = {ACM},\\n\\tauthor = {Harper, F Maxwell and Xu, Funing and Kaur, Harmanpreet and Condiff, Kyle and Chang, Shuo and Terveen, Loren},\\n\\tyear = {2015},\\n\\tnote = {Journal Abbreviation: RecSys '15},\\n\\tpages = {3--10},\\n}\\n\\n\",\"author_short\":[\"Harper, F M.\",\"Xu, F.\",\"Kaur, H.\",\"Condiff, K.\",\"Chang, S.\",\"Terveen, L.\"],\"key\":\"harper_putting_2015\",\"id\":\"harper_putting_2015\",\"bibbaseid\":\"harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2792838.2800179\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Minneapolis, MN\",\"title\":\"Towards Recommender Engineering: Tools and Experiments in Recommender Differences\",\"url\":\"http://hdl.handle.net/11299/165307\",\"abstract\":\"Since the introduction of their modern form 20 years ago, recommender systems have proven a valuable tool for help users manage information overload. Two decades of research have produced many algorithms for computing recommendations, mechanisms for evaluating their effectiveness, and user interfaces and experiences to embody them. It has also been found that the outputs of different recommendation algorithms differ in user-perceptible ways that affect their suitability to different tasks and information needs. However, there has been little work to systematically map out the space of algorithms and the characteristics they exhibit that makes them more or less effective in different applications. As a result, developers of recommender systems must experiment, conducting basic science on each application and its users to determine the approach(es) that will meet their needs. This thesis presents our work towards recommender engineering: the design of recommender systems from well-understood principles of user needs, domain properties, and algorithm behaviors. This will reduce the experimentation required for each new recommender application, allowing developers to design recommender systems that are likely to be effective for their particular application. To that end, we make four contributions: the LensKit toolkit for conducting experiments on a wide variety of recommender algorithms and data sets under different experimental conditions (offline experiments with diverse metrics, online user studies, and the ability to grow to support additional methodologies), along with new developments in object-oriented software configuration to support this toolkit; experiments on the configuration options of widely-used algorithms to provide guidance on tuning and configuring them; an offline experiment on the differences in the errors made by different algorithms; and a user study on the user-perceptible differences between lists of movie recommendations produced by three common recommender algorithms. Much research is needed to fully realize the vision of recommender engineering in the coming years; it is our hope that LensKit will prove a valuable foundation for much of this work, and our experiments represent a small piece of the kinds of studies that must be carried out, replicated, and validated to enable recommender systems to be engineered.\",\"school\":\"University of Minnesota\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]}],\"collaborator\":\"Konstan, Joseph A\",\"month\":\"July\",\"year\":\"2014\",\"note\":\"Publication Title: Computer Science and Engineering Volume: Ph.D\",\"bibtex\":\"@phdthesis{ekstrand_towards_2014,\\n\\taddress = {Minneapolis, MN},\\n\\ttitle = {Towards {Recommender} {Engineering}: {Tools} and {Experiments} in {Recommender} {Differences}},\\n\\turl = {http://hdl.handle.net/11299/165307},\\n\\tabstract = {Since the introduction of their modern form 20 years ago, recommender\\nsystems have proven a valuable tool for help users manage information\\noverload. Two decades of research have produced many algorithms for\\ncomputing recommendations, mechanisms for evaluating their effectiveness,\\nand user interfaces and experiences to embody them. It has also been found\\nthat the outputs of different recommendation algorithms differ in\\nuser-perceptible ways that affect their suitability to different tasks and\\ninformation needs. However, there has been little work to systematically\\nmap out the space of algorithms and the characteristics they exhibit that\\nmakes them more or less effective in different applications. As a result,\\ndevelopers of recommender systems must experiment, conducting basic\\nscience on each application and its users to determine the approach(es)\\nthat will meet their needs. This thesis presents our work towards\\nrecommender engineering: the design of recommender systems from\\nwell-understood principles of user needs, domain properties, and algorithm\\nbehaviors. This will reduce the experimentation required for each new\\nrecommender application, allowing developers to design recommender systems\\nthat are likely to be effective for their particular application. To that\\nend, we make four contributions: the LensKit toolkit for conducting\\nexperiments on a wide variety of recommender algorithms and data sets\\nunder different experimental conditions (offline experiments with diverse\\nmetrics, online user studies, and the ability to grow to support\\nadditional methodologies), along with new developments in object-oriented\\nsoftware configuration to support this toolkit; experiments on the\\nconfiguration options of widely-used algorithms to provide guidance on\\ntuning and configuring them; an offline experiment on the differences in\\nthe errors made by different algorithms; and a user study on the\\nuser-perceptible differences between lists of movie recommendations\\nproduced by three common recommender algorithms. Much research is needed\\nto fully realize the vision of recommender engineering in the coming\\nyears; it is our hope that LensKit will prove a valuable foundation for\\nmuch of this work, and our experiments represent a small piece of the\\nkinds of studies that must be carried out, replicated, and validated to\\nenable recommender systems to be engineered.},\\n\\tschool = {University of Minnesota},\\n\\tauthor = {Ekstrand, Michael D},\\n\\tcollaborator = {Konstan, Joseph A},\\n\\tmonth = jul,\\n\\tyear = {2014},\\n\\tnote = {Publication Title: Computer Science and Engineering\\nVolume: Ph.D},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\"],\"key\":\"ekstrand_towards_2014\",\"id\":\"ekstrand_towards_2014\",\"bibbaseid\":\"ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://hdl.handle.net/11299/165307\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"When recommenders fail: predicting recommender failure for algorithm selection and combination\",\"url\":\"http://doi.acm.org/10.1145/2365952.2366002\",\"doi\":\"10.1145/2365952.2366002\",\"abstract\":\"Hybrid recommender systems — systems using multiple algorithms together to improve recommendation quality — have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the relative performance of recommenders varies by circumstance and attempt to optimize each item score to maximize the strengths of the component recommenders. Less attention, however, has been paid to understanding what these strengths and failure modes are. Understanding what causes particular recommenders to fail will facilitate better selection of the component recommenders for future hybrid systems and a better understanding of how individual recommender personalities can be harnessed to improve the recommender user experience. We present an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.\",\"urldate\":\"2012-12-13\",\"booktitle\":\"RecSys '12\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Riedl\"],\"firstnames\":[\"John\",\"T\"],\"suffixes\":[]}],\"year\":\"2012\",\"note\":\"Journal Abbreviation: RecSys '12\",\"pages\":\"233–236\",\"bibtex\":\"@inproceedings{ekstrand_when_2012,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {When recommenders fail: predicting recommender failure for algorithm selection and combination},\\n\\turl = {http://doi.acm.org/10.1145/2365952.2366002},\\n\\tdoi = {10.1145/2365952.2366002},\\n\\tabstract = {Hybrid recommender systems --- systems using multiple algorithms together\\nto improve recommendation quality --- have been well-known for many years\\nand have shown good performance in recent demonstrations such as the\\nNetFlix Prize. Modern hybridization techniques, such as feature-weighted\\nlinear stacking, take advantage of the hypothesis that the relative\\nperformance of recommenders varies by circumstance and attempt to optimize\\neach item score to maximize the strengths of the component recommenders.\\nLess attention, however, has been paid to understanding what these\\nstrengths and failure modes are. Understanding what causes particular\\nrecommenders to fail will facilitate better selection of the component\\nrecommenders for future hybrid systems and a better understanding of how\\nindividual recommender personalities can be harnessed to improve the\\nrecommender user experience. We present an analysis of the predictions\\nmade by several well-known recommender algorithms on the MovieLens 10M\\ndata set, showing that for many cases in which one algorithm fails, there\\nis another that will correctly predict the rating.},\\n\\turldate = {2012-12-13},\\n\\tbooktitle = {{RecSys} '12},\\n\\tpublisher = {ACM},\\n\\tauthor = {Ekstrand, Michael D and Riedl, John T},\\n\\tyear = {2012},\\n\\tnote = {Journal Abbreviation: RecSys '12},\\n\\tpages = {233--236},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\",\"Riedl, J. T\"],\"key\":\"ekstrand_when_2012\",\"id\":\"ekstrand_when_2012\",\"bibbaseid\":\"ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2365952.2366002\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"How many bits per rating?\",\"url\":\"http://doi.acm.org/10.1145/2365952.2365974\",\"doi\":\"10.1145/2365952.2365974\",\"abstract\":\"Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.\",\"urldate\":\"2013-09-12\",\"booktitle\":\"RecSys '12\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kluver\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nguyen\"],\"firstnames\":[\"Tien\",\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sen\"],\"firstnames\":[\"Shilad\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Riedl\"],\"firstnames\":[\"John\"],\"suffixes\":[]}],\"year\":\"2012\",\"note\":\"Journal Abbreviation: RecSys '12\",\"pages\":\"99–106\",\"bibtex\":\"@inproceedings{kluver_how_2012,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {How many bits per rating?},\\n\\turl = {http://doi.acm.org/10.1145/2365952.2365974},\\n\\tdoi = {10.1145/2365952.2365974},\\n\\tabstract = {Most recommender systems assume user ratings accurately represent user\\npreferences. However, prior research shows that user ratings are imperfect\\nand noisy. Moreover, this noise limits the measurable predictive power of\\nany recommender system. We propose an information theoretic framework for\\nquantifying the preference information contained in ratings and\\npredictions. We computationally explore the properties of our model and\\napply our framework to estimate the efficiency of different rating scales\\nfor real world datasets. We then estimate how the amount of information\\npredictions give to users is related to the scale ratings are collected\\non. Our findings suggest a tradeoff in rating scale granularity: while\\nprevious research indicates that coarse scales (such as thumbs up / thumbs\\ndown) take less time, we find that ratings with these scales provide less\\npredictive value to users. We introduce a new measure, preference bits per\\nsecond, to quantitatively reconcile this tradeoff.},\\n\\turldate = {2013-09-12},\\n\\tbooktitle = {{RecSys} '12},\\n\\tpublisher = {ACM},\\n\\tauthor = {Kluver, Daniel and Nguyen, Tien T and Ekstrand, Michael and Sen, Shilad and Riedl, John},\\n\\tyear = {2012},\\n\\tnote = {Journal Abbreviation: RecSys '12},\\n\\tpages = {99--106},\\n}\\n\\n\",\"author_short\":[\"Kluver, D.\",\"Nguyen, T. T\",\"Ekstrand, M.\",\"Sen, S.\",\"Riedl, J.\"],\"key\":\"kluver_how_2012\",\"id\":\"kluver_how_2012\",\"bibbaseid\":\"kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2365952.2365974\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Rating Support Interfaces to Improve User Experience and Recommender Accuracy\",\"url\":\"http://doi.acm.org/10.1145/2507157.2507188\",\"doi\":\"10.1145/2507157.2507188\",\"abstract\":\"One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.\",\"urldate\":\"2014-04-28\",\"booktitle\":\"RecSys '13\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Nguyen\"],\"firstnames\":[\"Tien\",\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kluver\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Ting-Yu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hui\"],\"firstnames\":[\"Pik-Mai\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Willemsen\"],\"firstnames\":[\"Martijn\",\"C\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Riedl\"],\"firstnames\":[\"John\"],\"suffixes\":[]}],\"year\":\"2013\",\"note\":\"Journal Abbreviation: RecSys '13\",\"pages\":\"149–156\",\"bibtex\":\"@inproceedings{nguyen_rating_2013,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Rating {Support} {Interfaces} to {Improve} {User} {Experience} and {Recommender} {Accuracy}},\\n\\turl = {http://doi.acm.org/10.1145/2507157.2507188},\\n\\tdoi = {10.1145/2507157.2507188},\\n\\tabstract = {One of the challenges for recommender systems is that users struggle to\\naccurately map their internal preferences to external measures of quality\\nsuch as ratings. We study two methods for supporting the mapping process:\\n(i) reminding the user of characteristics of items by providing\\npersonalized tags and (ii) relating rating decisions to prior rating\\ndecisions using exemplars. In our study, we introduce interfaces that\\nprovide these methods of support. We also present a set of methodologies\\nto evaluate the efficacy of the new interfaces via a user experiment. Our\\nresults suggest that presenting exemplars during the rating process helps\\nusers rate more consistently, and increases the quality of the data.},\\n\\turldate = {2014-04-28},\\n\\tbooktitle = {{RecSys} '13},\\n\\tpublisher = {ACM},\\n\\tauthor = {Nguyen, Tien T and Kluver, Daniel and Wang, Ting-Yu and Hui, Pik-Mai and Ekstrand, Michael D and Willemsen, Martijn C and Riedl, John},\\n\\tyear = {2013},\\n\\tnote = {Journal Abbreviation: RecSys '13},\\n\\tpages = {149--156},\\n}\\n\\n\",\"author_short\":[\"Nguyen, T. T\",\"Kluver, D.\",\"Wang, T.\",\"Hui, P.\",\"Ekstrand, M. D\",\"Willemsen, M. C\",\"Riedl, J.\"],\"key\":\"nguyen_rating_2013\",\"id\":\"nguyen_rating_2013\",\"bibbaseid\":\"nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2507157.2507188\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC\",\"url\":\"http://doi.acm.org/10.1145/2556325.2566244\",\"doi\":\"10.1145/2556325.2566244\",\"abstract\":\"In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.\",\"urldate\":\"2014-03-19\",\"booktitle\":\"L@S '14\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Konstan\"],\"firstnames\":[\"Joseph\",\"A\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Walker\"],\"firstnames\":[\"J\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brooks\"],\"firstnames\":[\"D\",\"Christopher\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brown\"],\"firstnames\":[\"Keith\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2014\",\"note\":\"Journal Abbreviation: L@S '14\",\"pages\":\"61–70\",\"bibtex\":\"@inproceedings{konstan_teaching_2014,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Teaching {Recommender} {Systems} at {Large} {Scale}: {Evaluation} and {Lessons} {Learned} from a {Hybrid} {MOOC}},\\n\\turl = {http://doi.acm.org/10.1145/2556325.2566244},\\n\\tdoi = {10.1145/2556325.2566244},\\n\\tabstract = {In Fall 2013 we offered an open online Introduction to Recommender Systems\\nthrough Coursera, while simultaneously offering a for-credit version of\\nthe course on-campus using the Coursera platform and a flipped classroom\\ninstruction model. As the goal of offering this course was to experiment\\nwith this type of instruction, we performed extensive evaluation including\\nsurveys of demographics, self-assessed skills, and learning intent; we\\nalso designed a knowledge-assessment tool specifically for the subject\\nmatter in this course, administering it before and after the course to\\nmeasure learning. We also tracked students through the course, including\\nseparating out students enrolled for credit from those enrolled only for\\nthe free, open course. This article reports on our findings.},\\n\\turldate = {2014-03-19},\\n\\tbooktitle = {L@{S} '14},\\n\\tpublisher = {ACM},\\n\\tauthor = {Konstan, Joseph A and Walker, J D and Brooks, D Christopher and Brown, Keith and Ekstrand, Michael D},\\n\\tmonth = mar,\\n\\tyear = {2014},\\n\\tnote = {Journal Abbreviation: L@S '14},\\n\\tpages = {61--70},\\n}\\n\\n\",\"author_short\":[\"Konstan, J. A\",\"Walker, J D\",\"Brooks, D C.\",\"Brown, K.\",\"Ekstrand, M. D\"],\"key\":\"konstan_teaching_2014\",\"id\":\"konstan_teaching_2014\",\"bibbaseid\":\"konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2556325.2566244\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Scaling Collaborative Filtering to Large-Scale Bipartite Rating Graphs Using Lenskit and Spark\",\"url\":\"http://dx.doi.org/10.1109/BigDataService.2017.28\",\"doi\":\"10.1109/BigDataService.2017.28\",\"abstract\":\"Popular social networking applications such as Facebook, Twitter, Friendster, etc. generate very large graphs with different characteristics. These social networks are huge, comprising millions of nodes and edges that push existing graph mining algorithms and architectures to their limits. In product-rating graphs, users connect with each other and rate items in tandem. In such bipartite graphs users and items are the nodes and ratings are the edges and collaborative filtering algorithms use the edge information (i.e. user ratings for items) in order to suggest items of potential interest to users. Existing algorithms can hardly scale up to the size of the entire graph and require unlimited resources to finish. This work employs a machine learning method for predicting the performance of Collaborative Filtering algorithms using the structural features of the bipartite graphs. Using a fast graph partitioning algorithm and information from the user friendship graph, the original bipartite graph is partitioned into different schemes (i.e. sets of smaller bipartite graphs). The schemes are evaluated against the predicted performance of the Collaborative Filtering algorithm and the best partitioning scheme is employed for generating the recommendations. As a result, the Collaborative Filtering algorithms are applied to smaller bipartite graphs, using limited resources and allowing the problem to scale or be parallelized. Tests on a large, real-life, rating graph, show that the proposed method allows the collaborative filtering algorithms to run in parallel and complete using limited resources.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Sardianos\"],\"firstnames\":[\"C\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Varlamis\"],\"firstnames\":[\"I\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Eirinaki\"],\"firstnames\":[\"M\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2017\",\"keywords\":\"Bipartite graph, Collaboration, Collaborative Filtering, Graph Metrics, Graph Partitioning, Lenskit, Machine learning algorithms, Partitioning algorithms, Prediction algorithms, Recommender Systems, Recommender systems, Social Networks, Social network services, Spark, bipartite graphs, collaborative filtering, collaborative filtering algorithms, data mining, fast graph partitioning algorithm, graph theory, large-scale bipartite rating graphs, learning (artificial intelligence), machine learning, product-rating graphs, social networking (online), social networking applications, structural features, user-friendship graph\",\"pages\":\"70–79\",\"bibtex\":\"@inproceedings{sardianos_scaling_2017,\\n\\ttitle = {Scaling {Collaborative} {Filtering} to {Large}-{Scale} {Bipartite} {Rating} {Graphs} {Using} {Lenskit} and {Spark}},\\n\\turl = {http://dx.doi.org/10.1109/BigDataService.2017.28},\\n\\tdoi = {10.1109/BigDataService.2017.28},\\n\\tabstract = {Popular social networking applications such as Facebook, Twitter,\\nFriendster, etc. generate very large graphs with different\\ncharacteristics. These social networks are huge, comprising millions of\\nnodes and edges that push existing graph mining algorithms and\\narchitectures to their limits. In product-rating graphs, users connect\\nwith each other and rate items in tandem. In such bipartite graphs users\\nand items are the nodes and ratings are the edges and collaborative\\nfiltering algorithms use the edge information (i.e. user ratings for\\nitems) in order to suggest items of potential interest to users. Existing\\nalgorithms can hardly scale up to the size of the entire graph and require\\nunlimited resources to finish. This work employs a machine learning method\\nfor predicting the performance of Collaborative Filtering algorithms using\\nthe structural features of the bipartite graphs. Using a fast graph\\npartitioning algorithm and information from the user friendship graph, the\\noriginal bipartite graph is partitioned into different schemes (i.e. sets\\nof smaller bipartite graphs). The schemes are evaluated against the\\npredicted performance of the Collaborative Filtering algorithm and the\\nbest partitioning scheme is employed for generating the recommendations.\\nAs a result, the Collaborative Filtering algorithms are applied to smaller\\nbipartite graphs, using limited resources and allowing the problem to\\nscale or be parallelized. Tests on a large, real-life, rating graph, show\\nthat the proposed method allows the collaborative filtering algorithms to\\nrun in parallel and complete using limited resources.},\\n\\tauthor = {Sardianos, C and Varlamis, I and Eirinaki, M},\\n\\tmonth = apr,\\n\\tyear = {2017},\\n\\tkeywords = {Bipartite graph, Collaboration, Collaborative Filtering, Graph Metrics, Graph Partitioning, Lenskit, Machine learning algorithms, Partitioning algorithms, Prediction algorithms, Recommender Systems, Recommender systems, Social Networks, Social network services, Spark, bipartite graphs, collaborative filtering, collaborative filtering algorithms, data mining, fast graph partitioning algorithm, graph theory, large-scale bipartite rating graphs, learning (artificial intelligence), machine learning, product-rating graphs, social networking (online), social networking applications, structural features, user-friendship graph},\\n\\tpages = {70--79},\\n}\\n\\n\",\"author_short\":[\"Sardianos, C\",\"Varlamis, I\",\"Eirinaki, M\"],\"key\":\"sardianos_scaling_2017\",\"id\":\"sardianos_scaling_2017\",\"bibbaseid\":\"sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1109/BigDataService.2017.28\"},\"keyword\":[\"Bipartite graph\",\"Collaboration\",\"Collaborative Filtering\",\"Graph Metrics\",\"Graph Partitioning\",\"Lenskit\",\"Machine learning algorithms\",\"Partitioning algorithms\",\"Prediction algorithms\",\"Recommender Systems\",\"Recommender systems\",\"Social Networks\",\"Social network services\",\"Spark\",\"bipartite graphs\",\"collaborative filtering\",\"collaborative filtering algorithms\",\"data mining\",\"fast graph partitioning algorithm\",\"graph theory\",\"large-scale bipartite rating graphs\",\"learning (artificial intelligence)\",\"machine learning\",\"product-rating graphs\",\"social networking (online)\",\"social networking applications\",\"structural features\",\"user-friendship graph\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"book\",\"type\":\"book\",\"title\":\"Serendipity in recommender systems\",\"isbn\":\"978-951-39-7438-1\",\"url\":\"https://jyx.jyu.fi/handle/123456789/58207\",\"abstract\":\"The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are auxiliary systems designed to help users find interesting goods or services (items) on a website when the number of available items is overwhelming. Traditionally, recommender systems have been optimized for accuracy, which indicates how often a user consumed the items recommended by system. To increase accuracy, recommender systems often suggest items that are popular and suitably similar to items these users have consumed in the past. As a result, users often lose interest in using these systems, as they either know about the recommended items already or can easily find these items themselves. One way to increase user satisfaction and user retention is to suggest serendipitous items. These items are items that users would not find themselves or even look for, but would enjoy consuming. Serendipity in recommender systems has not been thoroughly investigated. There is not even a consensus on the concept’s definition. In this dissertation, serendipitous items are defined as relevant, novel and unexpected to a user. In this dissertation, we (a) review different definitions of the concept and evaluate them in a user study, (b) assess the proportion of serendipitous items in a typical recommender system, (c) review ways to measure and improve serendipity, (d) investigate serendipity in cross-domain recommender systems (systems that take advantage of multiple domains, such as movies, songs and books) and (e) discuss challenges and future directions concerning this topic. We applied a Design Science methodology as the framework for this study and developed four artifacts: (1) a collection of eight variations of serendipity definition, (2) a measure of the serendipity of suggested items, (3) an algorithm that generates serendipitous suggestions, (4) a dataset of user feedback regarding serendipitous movies in the recommender system MovieLens. These artifacts are evaluated using suitable methods and communicated through publications.\",\"urldate\":\"2018-07-06\",\"publisher\":\"University of Jyväskylä\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kotkov\"],\"firstnames\":[\"Denis\"],\"suffixes\":[]}],\"year\":\"2018\",\"bibtex\":\"@book{kotkov_serendipity_2018,\\n\\ttitle = {Serendipity in recommender systems},\\n\\tisbn = {978-951-39-7438-1},\\n\\turl = {https://jyx.jyu.fi/handle/123456789/58207},\\n\\tabstract = {The number of goods and services (such as accommodation or music\\nstreaming) offered by e-commerce websites does not allow users to examine\\nall the available options in a reasonable amount of time. Recommender\\nsystems are auxiliary systems designed to help users find interesting goods\\nor services (items) on a website when the number of available items is\\noverwhelming. Traditionally, recommender systems have been optimized for\\naccuracy, which indicates how often a user consumed the items recommended\\nby system. To increase accuracy, recommender systems often suggest items\\nthat are popular and suitably similar to items these users have consumed\\nin the past. As a result, users often lose interest in using these\\nsystems, as they either know about the recommended items already or can\\neasily find these items themselves. One way to increase user satisfaction\\nand user retention is to suggest serendipitous items. These items are\\nitems that users would not find themselves or even look for, but would\\nenjoy consuming. Serendipity in recommender systems has not been\\nthoroughly investigated. There is not even a consensus on the concept’s\\ndefinition. In this dissertation, serendipitous items are defined as\\nrelevant, novel and unexpected to a user. In this dissertation, we (a)\\nreview different definitions of the concept and evaluate them in a user\\nstudy, (b) assess the proportion of serendipitous items in a typical\\nrecommender system, (c) review ways to measure and improve serendipity,\\n(d) investigate serendipity in cross-domain recommender systems (systems\\nthat take advantage of multiple domains, such as movies, songs and books)\\nand (e) discuss challenges and future directions concerning this topic. We\\napplied a Design Science methodology as the framework for this study and\\ndeveloped four artifacts: (1) a collection of eight variations of\\nserendipity definition, (2) a measure of the serendipity of suggested\\nitems, (3) an algorithm that generates serendipitous suggestions, (4) a\\ndataset of user feedback regarding serendipitous movies in the recommender\\nsystem MovieLens. These artifacts are evaluated using suitable methods and\\ncommunicated through publications.},\\n\\turldate = {2018-07-06},\\n\\tpublisher = {University of Jyväskylä},\\n\\tauthor = {Kotkov, Denis},\\n\\tyear = {2018},\\n}\\n\\n\",\"author_short\":[\"Kotkov, D.\"],\"key\":\"kotkov_serendipity_2018\",\"id\":\"kotkov_serendipity_2018\",\"bibbaseid\":\"kotkov-serendipityinrecommendersystems-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://jyx.jyu.fi/handle/123456789/58207\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Ann Arbor, United States\",\"title\":\"Predictive Accuracy of Recommender Algorithms\",\"abstract\":\"Recommender systems present a customized list of items based upon user or item characteristics with the objective of reducing a large number of possible choices to a smaller ranked set most likely to appeal to the user. A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms, because studies evaluating different methods have not used a common set of benchmark data sets, baseline models, and evaluation metrics.The dissertation used publicly available sources of ratings data with a suite of three conventional recommender algorithms and two deep learning (DL) algorithms in controlled experiments to assess their comparative accuracy. Results for the non-DL algorithms conformed well to published results and benchmarks. The two DL algorithms did not perform as well and illuminated known challenges implementing DL recommender algorithms as reported in the literature. Model overfitting is discussed as a potential explanation for the weaker performance of the DL algorithms and several regularization strategies are reviewed as possible approaches to improve predictive error. Findings justify the need for further research in the use of deep learning models for recommender systems.\",\"school\":\"Nova Southeastern University\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Noffsinger\"],\"firstnames\":[\"William\",\"B\"],\"suffixes\":[]}],\"collaborator\":\"Mukherjee, Sumitra\",\"year\":\"2020\",\"note\":\"Publication Title: Information Systems (DISS)\",\"bibtex\":\"@phdthesis{noffsinger_predictive_2020,\\n\\taddress = {Ann Arbor, United States},\\n\\ttitle = {Predictive {Accuracy} of {Recommender} {Algorithms}},\\n\\turl = {https://libproxy.boisestate.edu/login?url=https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2},\\n\\tabstract = {Recommender systems present a customized list of items based upon user or\\nitem characteristics with the objective of reducing a large number of\\npossible choices to a smaller ranked set most likely to appeal to the\\nuser. A variety of algorithms for recommender systems have been developed\\nand refined including applications of deep learning neural networks.\\nRecent research reports point to a need to perform carefully controlled\\nexperiments to gain insights about the relative accuracy of different\\nrecommender algorithms, because studies evaluating different methods have\\nnot used a common set of benchmark data sets, baseline models, and\\nevaluation metrics.The dissertation used publicly available sources of\\nratings data with a suite of three conventional recommender algorithms and\\ntwo deep learning (DL) algorithms in controlled experiments to assess\\ntheir comparative accuracy. Results for the non-DL algorithms conformed\\nwell to published results and benchmarks. The two DL algorithms did not\\nperform as well and illuminated known challenges implementing DL\\nrecommender algorithms as reported in the literature. Model overfitting is\\ndiscussed as a potential explanation for the weaker performance of the DL\\nalgorithms and several regularization strategies are reviewed as possible\\napproaches to improve predictive error. Findings justify the need for\\nfurther research in the use of deep learning models for recommender\\nsystems.},\\n\\tschool = {Nova Southeastern University},\\n\\tauthor = {Noffsinger, William B},\\n\\tcollaborator = {Mukherjee, Sumitra},\\n\\tyear = {2020},\\n\\tnote = {Publication Title: Information Systems (DISS)},\\n}\\n\\n\",\"author_short\":[\"Noffsinger, W. B\"],\"urlhttps://libproxy.boisestate.edu/login?url\":\"https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2\",\"key\":\"noffsinger_predictive_2020\",\"id\":\"noffsinger_predictive_2020\",\"bibbaseid\":\"noffsinger-predictiveaccuracyofrecommenderalgorithms-2020\",\"role\":\"author\",\"urls\":{\"Https://libproxy.boisestate.edu/login?url\":\"https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A new similarity measure for collaborative filtering based recommender systems\",\"volume\":\"188\",\"issn\":\"0950-7051\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0950705119304484\",\"doi\":\"10.1016/j.knosys.2019.105058\",\"abstract\":\"The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.\",\"journal\":\"Knowledge-Based Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Gazdar\"],\"firstnames\":[\"Achraf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hidri\"],\"firstnames\":[\"Lotfi\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2020\",\"keywords\":\"Collaborative filtering, Neighborhood based CF, Recommendation systems, Similarity measure\",\"pages\":\"105058\",\"bibtex\":\"@article{gazdar_new_2020,\\n\\ttitle = {A new similarity measure for collaborative filtering based recommender systems},\\n\\tvolume = {188},\\n\\tissn = {0950-7051},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S0950705119304484},\\n\\tdoi = {10.1016/j.knosys.2019.105058},\\n\\tabstract = {The objective of a recommender system is to provide customers with\\npersonalized recommendations while selecting an item among a set of\\nproducts (movies, books, etc.). The collaborative filtering is the most\\nused technique for recommender systems. One of the main components of a\\nrecommender system based on the collaborative filtering technique, is the\\nsimilarity measure used to determine the set of users having the same\\nbehavior with regard to the selected items. Several similarity functions\\nhave been proposed, with different performances in terms of accuracy and\\nquality of recommendations. In this paper, we propose a new simple and\\nefficient similarity measure. Its mathematical expression is determined\\nthrough the following paper contributions: 1) transforming some intuitive\\nand qualitative conditions, that should be satisfied by the similarity\\nmeasure, into relevant mathematical equations namely: the integral\\nequation, the linear system of differential equations and a non-linear\\nsystem and 2) resolving the equations to achieve the kernel function of\\nthe similarity measure. The extensive experimental study driven on a\\nbenchmark datasets shows that the proposed similarity measure is very\\ncompetitive, especially in terms of accuracy, with regards to some\\nrepresentative similarity measures of the literature.},\\n\\tjournal = {Knowledge-Based Systems},\\n\\tauthor = {Gazdar, Achraf and Hidri, Lotfi},\\n\\tmonth = jan,\\n\\tyear = {2020},\\n\\tkeywords = {Collaborative filtering, Neighborhood based CF, Recommendation systems, Similarity measure},\\n\\tpages = {105058},\\n}\\n\\n\",\"author_short\":[\"Gazdar, A.\",\"Hidri, L.\"],\"key\":\"gazdar_new_2020\",\"id\":\"gazdar_new_2020\",\"bibbaseid\":\"gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0950705119304484\"},\"keyword\":[\"Collaborative filtering\",\"Neighborhood based CF\",\"Recommendation systems\",\"Similarity measure\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions\",\"url\":\"http://dx.doi.org/10.1007/978-3-030-39815-6_16\",\"doi\":\"10.1007/978-3-030-39815-6_16\",\"abstract\":\"It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Polychronou\"],\"firstnames\":[\"Ioanna\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Katsivelis\"],\"firstnames\":[\"Panagis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Papakonstantinou\"],\"firstnames\":[\"Mihalis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stoitsis\"],\"firstnames\":[\"Giannis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Manouselis\"],\"firstnames\":[\"Nikos\"],\"suffixes\":[]}],\"year\":\"2020\",\"pages\":\"165–172\",\"bibtex\":\"@inproceedings{polychronou_machine_2020,\\n\\ttitle = {Machine {Learning} {Algorithms} for {Food} {Intelligence}: {Towards} a {Method} for {More} {Accurate} {Predictions}},\\n\\turl = {http://dx.doi.org/10.1007/978-3-030-39815-6_16},\\n\\tdoi = {10.1007/978-3-030-39815-6_16},\\n\\tabstract = {It is evident that machine learning algorithms are being widely impacting\\nindustrial applications and platforms. Beyond typical research\\nexperimentation scenarios, there is a need for companies that wish to\\nenhance their online data and analytics solutions to incorporate ways in\\nwhich they can select, experiment, benchmark, parameterise and choose the\\nversion of a machine learning algorithm that seems to be most appropriate\\nfor their specific application context. In this paper, we describe such a\\nneed for a big data platform that supports food data analytics and\\nintelligence. More specifically, we introduce Agroknow’s big data platform\\nand identify the need to extend it with a flexible and interactive\\nexperimentation environment where different machine learning algorithms\\ncan be tested using a variation of synthetic and real data. A typical\\nusage scenario is described, based on our need to experiment with various\\nmachine learning algorithms to support price prediction for food products\\nand ingredients. The initial requirements for an experimentation\\nenvironment are also introduced.},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Polychronou, Ioanna and Katsivelis, Panagis and Papakonstantinou, Mihalis and Stoitsis, Giannis and Manouselis, Nikos},\\n\\tyear = {2020},\\n\\tpages = {165--172},\\n}\\n\\n\",\"author_short\":[\"Polychronou, I.\",\"Katsivelis, P.\",\"Papakonstantinou, M.\",\"Stoitsis, G.\",\"Manouselis, N.\"],\"key\":\"polychronou_machine_2020\",\"id\":\"polychronou_machine_2020\",\"bibbaseid\":\"polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1007/978-3-030-39815-6_16\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Things you might not know about the k-Nearest neighbors algorithm\",\"url\":\"https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karpus\"],\"firstnames\":[\"Aleksandra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Raczyńska\"],\"firstnames\":[\"M\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Przybyłek\"],\"firstnames\":[\"A\"],\"suffixes\":[]}],\"year\":\"2019\",\"bibtex\":\"@inproceedings{karpus_things_2019,\\n\\ttitle = {Things you might not know about the k-{Nearest} neighbors algorithm},\\n\\turl = {https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf},\\n\\tauthor = {Karpus, Aleksandra and Raczyńska, M and Przybyłek, A},\\n\\tyear = {2019},\\n}\\n\\n\",\"author_short\":[\"Karpus, A.\",\"Raczyńska, M\",\"Przybyłek, A\"],\"key\":\"karpus_things_2019\",\"id\":\"karpus_things_2019\",\"bibbaseid\":\"karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"SCoR: A Synthetic Coordinate based Recommender system\",\"volume\":\"79\",\"issn\":\"0957-4174\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S0957417417301070\",\"doi\":\"10.1016/j.eswa.2017.02.025\",\"abstract\":\"Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer preferences. In this paper, we propose SCoR, a Synthetic Coordinate based Recommendation system which is shown to outperform the most popular algorithmic techniques in the field, approaches like matrix factorization and collaborative filtering. SCoR assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms. Furthermore, it provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers. SCoR is compared against nine other state-of-the-art recommender systems, sever of them based on the well known matrix factorization and two on collaborative filtering. The comparison is performed against four real datasets, including a brief version of the dataset used in the well known Netflix challenge. The extensive experiments prove that SCoR outperforms previous techniques while demonstrating its improved stability and high performance.\",\"journal\":\"Expert Syst. Appl.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Papadakis\"],\"firstnames\":[\"Harris\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Panagiotakis\"],\"firstnames\":[\"Costas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fragopoulou\"],\"firstnames\":[\"Paraskevi\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2017\",\"keywords\":\"Graph, Matrix factorization, Netflix, Recommender systems, Synthetic coordinates, Vivaldi\",\"pages\":\"8–19\",\"bibtex\":\"@article{papadakis_scor_2017,\\n\\ttitle = {{SCoR}: {A} {Synthetic} {Coordinate} based {Recommender} system},\\n\\tvolume = {79},\\n\\tissn = {0957-4174},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S0957417417301070},\\n\\tdoi = {10.1016/j.eswa.2017.02.025},\\n\\tabstract = {Recommender systems try to predict the preferences of users for specific\\nitems, based on an analysis of previous consumer preferences. In this\\npaper, we propose SCoR, a Synthetic Coordinate based Recommendation system\\nwhich is shown to outperform the most popular algorithmic techniques in\\nthe field, approaches like matrix factorization and collaborative\\nfiltering. SCoR assigns synthetic coordinates to nodes (users and items),\\nso that the distance between a user and an item provides an accurate\\nprediction of the user’s preference for that item. The proposed framework\\nhas several benefits. It is parameter free, thus requiring no fine tuning\\nto achieve high performance, and is more resistance to the cold-start\\nproblem compared to other algorithms. Furthermore, it provides important\\nannotations of the dataset, such as the physical detection of users and\\nitems with common and unique characteristics as well as the identification\\nof outliers. SCoR is compared against nine other state-of-the-art\\nrecommender systems, sever of them based on the well known matrix\\nfactorization and two on collaborative filtering. The comparison is\\nperformed against four real datasets, including a brief version of the\\ndataset used in the well known Netflix challenge. The extensive\\nexperiments prove that SCoR outperforms previous techniques while\\ndemonstrating its improved stability and high performance.},\\n\\tjournal = {Expert Syst. Appl.},\\n\\tauthor = {Papadakis, Harris and Panagiotakis, Costas and Fragopoulou, Paraskevi},\\n\\tmonth = aug,\\n\\tyear = {2017},\\n\\tkeywords = {Graph, Matrix factorization, Netflix, Recommender systems, Synthetic coordinates, Vivaldi},\\n\\tpages = {8--19},\\n}\\n\\n\",\"author_short\":[\"Papadakis, H.\",\"Panagiotakis, C.\",\"Fragopoulou, P.\"],\"key\":\"papadakis_scor_2017\",\"id\":\"papadakis_scor_2017\",\"bibbaseid\":\"papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S0957417417301070\"},\"keyword\":[\"Graph\",\"Matrix factorization\",\"Netflix\",\"Recommender systems\",\"Synthetic coordinates\",\"Vivaldi\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"mastersthesis\",\"type\":\"mastersthesis\",\"title\":\"Video Recommendation Systems: Finding a Suitable Recommendation Approach for an Application Without Sufficient Data\",\"url\":\"http://hdl.handle.net/10852/59239\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Solvang\"],\"firstnames\":[\"Marius\",\"Lørstad\"],\"suffixes\":[]}],\"year\":\"2017\",\"bibtex\":\"@mastersthesis{solvang_video_2017,\\n\\ttitle = {Video {Recommendation} {Systems}: {Finding} a {Suitable} {Recommendation} {Approach} for an {Application} {Without} {Sufficient} {Data}},\\n\\turl = {http://hdl.handle.net/10852/59239},\\n\\tauthor = {Solvang, Marius Lørstad},\\n\\tyear = {2017},\\n}\\n\\n\",\"author_short\":[\"Solvang, M. L.\"],\"key\":\"solvang_video_2017\",\"id\":\"solvang_video_2017\",\"bibbaseid\":\"solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://hdl.handle.net/10852/59239\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Recommending books to be exchanged online in the absence of wish lists\",\"issn\":\"2330-1643\",\"url\":\"http://dx.doi.org/10.1002/asi.23978\",\"doi\":\"10.1002/asi.23978\",\"abstract\":\"An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book-exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation-based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book-exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation-based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item-exchange mechanism with a new design methodology.\",\"journal\":\"Journal of the Association for Information Science and Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pera\"],\"firstnames\":[\"Maria\",\"Soledad\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ng\"],\"firstnames\":[\"Yiu-Kai\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2017\",\"bibtex\":\"@article{pera_recommending_2017,\\n\\ttitle = {Recommending books to be exchanged online in the absence of wish lists},\\n\\tissn = {2330-1643},\\n\\turl = {http://dx.doi.org/10.1002/asi.23978},\\n\\tdoi = {10.1002/asi.23978},\\n\\tabstract = {An online exchange system is a web service that allows communities to\\ntrade items without the burden of manually selecting them, which saves\\nusers' time and effort. Even though online book-exchange systems have been\\ndeveloped, their services can further be improved by reducing the workload\\nimposed on their users. To accomplish this task, we propose a\\nrecommendation-based book exchange system, called EasyEx, which identifies\\npotential exchanges for a user solely based on a list of items the user is\\nwilling to part with. EasyEx is a novel and unique book-exchange system\\nbecause unlike existing online exchange systems, it does not require a\\nuser to create and maintain a wish list, which is a list of items the user\\nwould like to receive as part of the exchange. Instead, EasyEx directly\\nsuggests items to users to increase serendipity and as a result expose\\nthem to items which may be unfamiliar, but appealing, to them. In\\nidentifying books to be exchanged, EasyEx employs known recommendation\\nstrategies, that is, personalized mean and matrix factorization, to\\npredict book ratings, which are treated as the degrees of appeal to a user\\non recommended books. Furthermore, EasyEx incorporates OptaPlanner, which\\nsolves constraint satisfaction problems efficiently, as part of the\\nrecommendation-based exchange process to create exchange cycles.\\nExperimental results have verified that EasyEx offers users recommended\\nbooks that satisfy the users' interests and contributes to the\\nitem-exchange mechanism with a new design methodology.},\\n\\tjournal = {Journal of the Association for Information Science and Technology},\\n\\tauthor = {Pera, Maria Soledad and Ng, Yiu-Kai},\\n\\tmonth = nov,\\n\\tyear = {2017},\\n}\\n\\n\",\"author_short\":[\"Pera, M. S.\",\"Ng, Y.\"],\"key\":\"pera_recommending_2017\",\"id\":\"pera_recommending_2017\",\"bibbaseid\":\"pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1002/asi.23978\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Recommendation system based contextual analysis of Facebook comment\",\"url\":\"http://dx.doi.org/10.1109/AICCSA.2016.7945792\",\"doi\":\"10.1109/AICCSA.2016.7945792\",\"abstract\":\"This paper present a new recommendation algorithm based on contextual analysis and new measurements. Social Network is one of the most popular Web 2.0 applications and related services, like Facebook, have evolved into a practical means for sharing opinions. Consequently, Social Network web sites have since become rich data sources for opinion mining. This paper proposes to introduce external resource from comments posted by users to predict recommendation and relieve the cold start problem. The novelty of the proposed approach is that posts are not simply characterized by an opinion score, as is the case with machine learning-based classifiers, but instead receive an opinion grade for each distinct notion in the post. Our approach has been implemented with Java and Lenskit framework; the study we have conducted on a movie dataset has shown competitive results. We compared our algorithm to SVD and Slope One algorithms. We have obtained an improvement of 8% in precision and recall as well an improvement of 16% in RMSE and nDCG.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kharrat\"],\"firstnames\":[\"F\",\"Ben\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Elkhleifi\"],\"firstnames\":[\"A\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Faiz\"],\"firstnames\":[\"R\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2016\",\"keywords\":\"Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile\",\"pages\":\"1–6\",\"bibtex\":\"@inproceedings{kharrat_recommendation_2016,\\n\\ttitle = {Recommendation system based contextual analysis of {Facebook} comment},\\n\\turl = {http://dx.doi.org/10.1109/AICCSA.2016.7945792},\\n\\tdoi = {10.1109/AICCSA.2016.7945792},\\n\\tabstract = {This paper present a new recommendation algorithm based on contextual\\nanalysis and new measurements. Social Network is one of the most popular\\nWeb 2.0 applications and related services, like Facebook, have evolved\\ninto a practical means for sharing opinions. Consequently, Social Network\\nweb sites have since become rich data sources for opinion mining. This\\npaper proposes to introduce external resource from comments posted by\\nusers to predict recommendation and relieve the cold start problem. The\\nnovelty of the proposed approach is that posts are not simply\\ncharacterized by an opinion score, as is the case with machine\\nlearning-based classifiers, but instead receive an opinion grade for each\\ndistinct notion in the post. Our approach has been implemented with Java\\nand Lenskit framework; the study we have conducted on a movie dataset has\\nshown competitive results. We compared our algorithm to SVD and Slope One\\nalgorithms. We have obtained an improvement of 8\\\\% in precision and recall\\nas well an improvement of 16\\\\% in RMSE and nDCG.},\\n\\tauthor = {Kharrat, F Ben and Elkhleifi, A and Faiz, R},\\n\\tmonth = nov,\\n\\tyear = {2016},\\n\\tkeywords = {Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile},\\n\\tpages = {1--6},\\n}\\n\\n\",\"author_short\":[\"Kharrat, F B.\",\"Elkhleifi, A\",\"Faiz, R\"],\"key\":\"kharrat_recommendation_2016\",\"id\":\"kharrat_recommendation_2016\",\"bibbaseid\":\"kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1109/AICCSA.2016.7945792\"},\"keyword\":[\"Algorithm design and analysis\",\"Classification algorithms\",\"Collaboration\",\"Collaborative filtering\",\"Facebook\",\"Motion pictures\",\"Recommendation system\",\"Recommender systems\",\"Social network\",\"User cold start\",\"User profile\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Scalable Similarity-based Neighborhood Methods with MapReduce\",\"url\":\"http://doi.acm.org/10.1145/2365952.2365984\",\"doi\":\"10.1145/2365952.2365984\",\"abstract\":\"Similarity-based neighborhood methods, a simple and popular approach to collaborative filtering, infer their predictions by finding users with similar taste or items that have been similarly rated. If the number of users grows to millions, the standard approach of sequentially examining each item and looking at all interacting users does not scale. To solve this problem, we develop a MapReduce algorithm for the pairwise item comparison and top-N recommendation problem that scales linearly with respect to a growing number of users. This parallel algorithm is able to work on partitioned data and is general in that it supports a wide range of similarity measures. We evaluate our algorithm on a large dataset consisting of 700 million song ratings from Yahoo! Music.\",\"urldate\":\"2015-09-23\",\"booktitle\":\"RecSys '12\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Schelter\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Boden\"],\"firstnames\":[\"Christoph\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Markl\"],\"firstnames\":[\"Volker\"],\"suffixes\":[]}],\"year\":\"2012\",\"note\":\"Journal Abbreviation: RecSys '12\",\"pages\":\"163–170\",\"bibtex\":\"@inproceedings{schelter_scalable_2012,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Scalable {Similarity}-based {Neighborhood} {Methods} with {MapReduce}},\\n\\turl = {http://doi.acm.org/10.1145/2365952.2365984},\\n\\tdoi = {10.1145/2365952.2365984},\\n\\tabstract = {Similarity-based neighborhood methods, a simple and popular approach to\\ncollaborative filtering, infer their predictions by finding users with\\nsimilar taste or items that have been similarly rated. If the number of\\nusers grows to millions, the standard approach of sequentially examining\\neach item and looking at all interacting users does not scale. To solve\\nthis problem, we develop a MapReduce algorithm for the pairwise item\\ncomparison and top-N recommendation problem that scales linearly with\\nrespect to a growing number of users. This parallel algorithm is able to\\nwork on partitioned data and is general in that it supports a wide range\\nof similarity measures. We evaluate our algorithm on a large dataset\\nconsisting of 700 million song ratings from Yahoo! Music.},\\n\\turldate = {2015-09-23},\\n\\tbooktitle = {{RecSys} '12},\\n\\tpublisher = {ACM},\\n\\tauthor = {Schelter, Sebastian and Boden, Christoph and Markl, Volker},\\n\\tyear = {2012},\\n\\tnote = {Journal Abbreviation: RecSys '12},\\n\\tpages = {163--170},\\n}\\n\\n\",\"author_short\":[\"Schelter, S.\",\"Boden, C.\",\"Markl, V.\"],\"key\":\"schelter_scalable_2012\",\"id\":\"schelter_scalable_2012\",\"bibbaseid\":\"schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2365952.2365984\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Using Groups of Items for Preference Elicitation in Recommender Systems\",\"url\":\"http://doi.acm.org/10.1145/2675133.2675210\",\"doi\":\"10.1145/2675133.2675210\",\"abstract\":\"To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items - this process is an inefficient way to convert a user's effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.\",\"urldate\":\"2015-09-19\",\"booktitle\":\"CSCW '15\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chang\"],\"firstnames\":[\"Shuo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Harper\"],\"firstnames\":[\"F\",\"Maxwell\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Terveen\"],\"firstnames\":[\"Loren\"],\"suffixes\":[]}],\"year\":\"2015\",\"note\":\"Journal Abbreviation: CSCW '15\",\"pages\":\"1258–1269\",\"bibtex\":\"@inproceedings{chang_using_2015,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Using {Groups} of {Items} for {Preference} {Elicitation} in {Recommender} {Systems}},\\n\\turl = {http://doi.acm.org/10.1145/2675133.2675210},\\n\\tdoi = {10.1145/2675133.2675210},\\n\\tabstract = {To achieve high quality initial personalization, recommender systems must\\nprovide an efficient and effective process for new users to express their\\npreferences. We propose that this goal is best served not by the classical\\nmethod where users begin by expressing preferences for individual items -\\nthis process is an inefficient way to convert a user's effort into\\nimproved personalization. Rather, we propose that new users can begin by\\nexpressing their preferences for groups of items. We test this idea by\\ndesigning and evaluating an interactive process where users express\\npreferences across groups of items that are automatically generated by\\nclustering algorithms. We contribute a strategy for recommending items\\nbased on these preferences that is generalizable to any collaborative\\nfiltering-based system. We evaluate our process with both offline\\nsimulation methods and an online user experiment. We find that, as\\ncompared with a baseline rate-15-items interface, (a) users are able to\\ncomplete the preference elicitation process in less than half the time,\\nand (b) users are more satisfied with the resulting recommended items. Our\\nevaluation reveals several advantages and other trade-offs involved in\\nmoving from item-based preference elicitation to group-based preference\\nelicitation.},\\n\\turldate = {2015-09-19},\\n\\tbooktitle = {{CSCW} '15},\\n\\tpublisher = {ACM},\\n\\tauthor = {Chang, Shuo and Harper, F Maxwell and Terveen, Loren},\\n\\tyear = {2015},\\n\\tnote = {Journal Abbreviation: CSCW '15},\\n\\tpages = {1258--1269},\\n}\\n\\n\",\"author_short\":[\"Chang, S.\",\"Harper, F M.\",\"Terveen, L.\"],\"key\":\"chang_using_2015\",\"id\":\"chang_using_2015\",\"bibbaseid\":\"chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2675133.2675210\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"User Perception of Differences in Movie Recommendation Algorithms\",\"url\":\"http://dx.doi.org/10.1145/2645710.2645737\",\"doi\":\"10.1145/2645710.2645737\",\"abstract\":\"Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from different recommender systems due to the many options in design and implementation of an evaluation strategy. Additionally, algorithm implementations can diverge from the standard formulation due to manual tuning and modifications that work better in some situations. In this work we compare common recommendation algorithms as implemented in three popular recommendation frameworks. To provide a fair comparison, we have complete control of the evaluation dimensions being benchmarked: dataset, data splitting, evaluation strategies, and metrics. We also include results using the internal evaluation mechanisms of these frameworks. Our analysis points to large differences in recommendation accuracy across frameworks and strategies, i.e. the same baselines may perform orders of magnitude better or worse across frameworks. Our results show the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results.\",\"booktitle\":\"RecSys '14\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ekstrand\"],\"firstnames\":[\"Michael\",\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Harper\"],\"firstnames\":[\"F\",\"Maxwell\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Willemsen\"],\"firstnames\":[\"Martijn\",\"C\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Konstan\"],\"firstnames\":[\"Joseph\",\"A\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2014\",\"note\":\"Journal Abbreviation: RecSys '14\",\"pages\":\"161–168\",\"bibtex\":\"@inproceedings{ekstrand_user_2014,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {User {Perception} of {Differences} in {Movie} {Recommendation} {Algorithms}},\\n\\turl = {http://dx.doi.org/10.1145/2645710.2645737},\\n\\tdoi = {10.1145/2645710.2645737},\\n\\tabstract = {Recommender systems research is often based on comparisons of predictive\\naccuracy: the better the evaluation scores, the better the recommender.\\nHowever, it is difficult to compare results from different recommender\\nsystems due to the many options in design and implementation of an\\nevaluation strategy. Additionally, algorithm implementations can diverge\\nfrom the standard formulation due to manual tuning and modifications that\\nwork better in some situations. In this work we compare common\\nrecommendation algorithms as implemented in three popular recommendation\\nframeworks. To provide a fair comparison, we have complete control of the\\nevaluation dimensions being benchmarked: dataset, data splitting,\\nevaluation strategies, and metrics. We also include results using the\\ninternal evaluation mechanisms of these frameworks. Our analysis points to\\nlarge differences in recommendation accuracy across frameworks and\\nstrategies, i.e. the same baselines may perform orders of magnitude better\\nor worse across frameworks. Our results show the necessity of clear\\nguidelines when reporting evaluation of recommender systems to ensure\\nreproducibility and comparison of results.},\\n\\tbooktitle = {{RecSys} '14},\\n\\tpublisher = {ACM},\\n\\tauthor = {Ekstrand, Michael D and Harper, F Maxwell and Willemsen, Martijn C and Konstan, Joseph A},\\n\\tmonth = oct,\\n\\tyear = {2014},\\n\\tnote = {Journal Abbreviation: RecSys '14},\\n\\tpages = {161--168},\\n}\\n\\n\",\"author_short\":[\"Ekstrand, M. D\",\"Harper, F M.\",\"Willemsen, M. C\",\"Konstan, J. A\"],\"key\":\"ekstrand_user_2014\",\"id\":\"ekstrand_user_2014\",\"bibbaseid\":\"ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1145/2645710.2645737\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Evaluating Recommender Behavior for New Users\",\"url\":\"http://dx.doi.org/10.1145/2645710.2645742\",\"doi\":\"10.1145/2645710.2645742\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kluver\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Konstan\"],\"firstnames\":[\"Joseph\",\"A\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2014\",\"bibtex\":\"@inproceedings{kluver_evaluating_2014,\\n\\ttitle = {Evaluating {Recommender} {Behavior} for {New} {Users}},\\n\\turl = {http://dx.doi.org/10.1145/2645710.2645742},\\n\\tdoi = {10.1145/2645710.2645742},\\n\\tpublisher = {ACM},\\n\\tauthor = {Kluver, Daniel and Konstan, Joseph A},\\n\\tmonth = oct,\\n\\tyear = {2014},\\n}\\n\\n\",\"author_short\":[\"Kluver, D.\",\"Konstan, J. A\"],\"key\":\"kluver_evaluating_2014\",\"id\":\"kluver_evaluating_2014\",\"bibbaseid\":\"kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1145/2645710.2645742\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A Personalized Concept-driven Recommender System for Scientific Libraries\",\"volume\":\"38\",\"issn\":\"1877-0509\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S1877050914013751\",\"doi\":\"10.1016/j.procs.2014.10.015\",\"abstract\":\"Recommender Systems can greatly enhance the exploitation of large digital libraries; however, in order to achieve good accuracy with collaborative recommenders some domain assumptions must be met, such as having a large number of users sharing similar interests over time. Such assumptions may not hold in digital libraries, where users are structured in relatively small groups of experts whose interests may change in unpredictable ways: this is the case of scientific and technical documents archives. Moreover, when recommending documents, users often expect insights on the recommended content as well as a detailed explanation of why the system has selected it, which cannot be provided by collaborative techniques. In this paper we consider the domain of scientific publications repositories and propose a content-based recommender based upon a graph representation of concepts built up by linked keyphrases. This recommender is coupled with a keyphrase extraction system able to generate meaningful metadata for the documents, which are the basis for providing helpful and explainable recommendations.\",\"urldate\":\"2015-09-23\",\"journal\":\"Procedia Comput. Sci.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"De\",\"Nart\"],\"firstnames\":[\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tasso\"],\"firstnames\":[\"C\"],\"suffixes\":[]}],\"year\":\"2014\",\"pages\":\"84–91\",\"bibtex\":\"@article{de_nart_personalized_2014,\\n\\ttitle = {A {Personalized} {Concept}-driven {Recommender} {System} for {Scientific} {Libraries}},\\n\\tvolume = {38},\\n\\tissn = {1877-0509},\\n\\turl = {http://www.sciencedirect.com/science/article/pii/S1877050914013751},\\n\\tdoi = {10.1016/j.procs.2014.10.015},\\n\\tabstract = {Recommender Systems can greatly enhance the exploitation of large digital\\nlibraries; however, in order to achieve good accuracy with collaborative\\nrecommenders some domain assumptions must be met, such as having a large\\nnumber of users sharing similar interests over time. Such assumptions may\\nnot hold in digital libraries, where users are structured in relatively\\nsmall groups of experts whose interests may change in unpredictable ways:\\nthis is the case of scientific and technical documents archives. Moreover,\\nwhen recommending documents, users often expect insights on the\\nrecommended content as well as a detailed explanation of why the system\\nhas selected it, which cannot be provided by collaborative techniques. In\\nthis paper we consider the domain of scientific publications repositories\\nand propose a content-based recommender based upon a graph representation\\nof concepts built up by linked keyphrases. This recommender is coupled\\nwith a keyphrase extraction system able to generate meaningful metadata\\nfor the documents, which are the basis for providing helpful and\\nexplainable recommendations.},\\n\\turldate = {2015-09-23},\\n\\tjournal = {Procedia Comput. Sci.},\\n\\tauthor = {De Nart, D and Tasso, C},\\n\\tyear = {2014},\\n\\tpages = {84--91},\\n}\\n\\n\",\"author_short\":[\"De Nart, D\",\"Tasso, C\"],\"key\":\"de_nart_personalized_2014\",\"id\":\"de_nart_personalized_2014\",\"bibbaseid\":\"denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S1877050914013751\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Improving Recommender Systems: User Roles and Lifecycles\",\"url\":\"http://doi.acm.org/10.1145/2645710.2653363\",\"doi\":\"10.1145/2645710.2653363\",\"abstract\":\"In the era of big data, it is usually agreed that the more data we have, the better results we can get. However, for some domains that heavily depend on user inputs (such as recommender systems), the performance evaluation metrics are sensitive to the amount of noise introduced by users. Such noise can be from users who only wanted to explore the systems, and thus did not spend efforts to provide accurate inputs. Noise can also be introduced by the methods of collecting user ratings. In my dissertation, I study how user data can affect prediction accuracies and performances of recommendation algorithms. To that end, I investigate how the data collection methods and the life cycles of users affect the prediction accuracies and the performance of recommendation algorithms.\",\"urldate\":\"2015-09-23\",\"booktitle\":\"RecSys '14\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Nguyen\"],\"firstnames\":[\"Tien\",\"T\"],\"suffixes\":[]}],\"year\":\"2014\",\"note\":\"Journal Abbreviation: RecSys '14\",\"pages\":\"417–420\",\"bibtex\":\"@inproceedings{nguyen_improving_2014,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Improving {Recommender} {Systems}: {User} {Roles} and {Lifecycles}},\\n\\turl = {http://doi.acm.org/10.1145/2645710.2653363},\\n\\tdoi = {10.1145/2645710.2653363},\\n\\tabstract = {In the era of big data, it is usually agreed that the more data we have,\\nthe better results we can get. However, for some domains that heavily\\ndepend on user inputs (such as recommender systems), the performance\\nevaluation metrics are sensitive to the amount of noise introduced by\\nusers. Such noise can be from users who only wanted to explore the\\nsystems, and thus did not spend efforts to provide accurate inputs. Noise\\ncan also be introduced by the methods of collecting user ratings. In my\\ndissertation, I study how user data can affect prediction accuracies and\\nperformances of recommendation algorithms. To that end, I investigate how\\nthe data collection methods and the life cycles of users affect the\\nprediction accuracies and the performance of recommendation algorithms.},\\n\\turldate = {2015-09-23},\\n\\tbooktitle = {{RecSys} '14},\\n\\tpublisher = {ACM},\\n\\tauthor = {Nguyen, Tien T},\\n\\tyear = {2014},\\n\\tnote = {Journal Abbreviation: RecSys '14},\\n\\tpages = {417--420},\\n}\\n\\n\",\"author_short\":[\"Nguyen, T. T\"],\"key\":\"nguyen_improving_2014\",\"id\":\"nguyen_improving_2014\",\"bibbaseid\":\"nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2645710.2653363\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"title\":\"Event Recommendation Using Twitter Activity\",\"url\":\"http://doi.acm.org/10.1145/2792838.2796556\",\"doi\":\"10.1145/2792838.2796556\",\"abstract\":\"User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.\",\"urldate\":\"2015-09-19\",\"booktitle\":\"RecSys '15\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Magnuson\"],\"firstnames\":[\"Axel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dialani\"],\"firstnames\":[\"Vijay\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mallela\"],\"firstnames\":[\"Deepa\"],\"suffixes\":[]}],\"year\":\"2015\",\"note\":\"Journal Abbreviation: RecSys '15\",\"pages\":\"331–332\",\"bibtex\":\"@inproceedings{magnuson_event_2015,\\n\\taddress = {New York, NY, USA},\\n\\ttitle = {Event {Recommendation} {Using} {Twitter} {Activity}},\\n\\turl = {http://doi.acm.org/10.1145/2792838.2796556},\\n\\tdoi = {10.1145/2792838.2796556},\\n\\tabstract = {User interactions with Twitter (social network) frequently take place on\\nmobile devices - a user base that it strongly caters to. As much of\\nTwitter's traffic comes with geo-tagging information associated with it,\\nit is a natural platform for geographic recommendations. This paper\\nproposes an event recommender system for Twitter users, which identifies\\ntwitter activity co-located with previous events, and uses it to drive\\ngeographic recommendations via item-based collaborative filtering.},\\n\\turldate = {2015-09-19},\\n\\tbooktitle = {{RecSys} '15},\\n\\tpublisher = {ACM},\\n\\tauthor = {Magnuson, Axel and Dialani, Vijay and Mallela, Deepa},\\n\\tyear = {2015},\\n\\tnote = {Journal Abbreviation: RecSys '15},\\n\\tpages = {331--332},\\n}\\n\\n\",\"author_short\":[\"Magnuson, A.\",\"Dialani, V.\",\"Mallela, D.\"],\"key\":\"magnuson_event_2015\",\"id\":\"magnuson_event_2015\",\"bibbaseid\":\"magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://doi.acm.org/10.1145/2792838.2796556\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"ICST, Brussels, Belgium, Belgium\",\"title\":\"Privacy-aware Location Privacy Preference Recommendations\",\"url\":\"http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017\",\"doi\":\"10.4108/icst.mobiquitous.2014.258017\",\"abstract\":\"Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people's sensitive location data in such applications is an important requirement. Conventional location privacy protection methods, however, such as manually defining privacy rules or asking users to make decisions each time they enter a new location may be overly complex, intrusive or unwieldy. An alternative is to use machine learning to predict people's privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a real-world location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people's preferences. Our results show that obfuscation leads to a minimal loss of prediction accuracy (0.76%).\",\"urldate\":\"2015-09-23\",\"booktitle\":\"MOBIQUITOUS '14\",\"publisher\":\"ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Zhao\"],\"firstnames\":[\"Yuchen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ye\"],\"firstnames\":[\"Juan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Henderson\"],\"firstnames\":[\"Tristan\"],\"suffixes\":[]}],\"year\":\"2014\",\"note\":\"Journal Abbreviation: MOBIQUITOUS '14\",\"pages\":\"120–129\",\"bibtex\":\"@inproceedings{zhao_privacy-aware_2014,\\n\\taddress = {ICST, Brussels, Belgium, Belgium},\\n\\ttitle = {Privacy-aware {Location} {Privacy} {Preference} {Recommendations}},\\n\\turl = {http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017},\\n\\tdoi = {10.4108/icst.mobiquitous.2014.258017},\\n\\tabstract = {Location-Based Services have become increasingly popular due to the\\nprevalence of smart devices and location-sharing applications such as\\nFacebook and Foursquare. The protection of people's sensitive location\\ndata in such applications is an important requirement. Conventional\\nlocation privacy protection methods, however, such as manually defining\\nprivacy rules or asking users to make decisions each time they enter a new\\nlocation may be overly complex, intrusive or unwieldy. An alternative is\\nto use machine learning to predict people's privacy preferences and\\nautomatically configure settings. Model-based machine learning classifiers\\nmay be too computationally complex to be used in real-world applications,\\nor suffer from poor performance when training data are insufficient. In\\nthis paper we propose a location-privacy recommender that can provide\\npeople with recommendations of appropriate location privacy settings\\nthrough user-user collaborative filtering. Using a real-world\\nlocation-sharing dataset, we show that the prediction accuracy of our\\nscheme (73.08\\\\%) is similar to the best performance of model-based\\nclassifiers (75.30\\\\%), and at the same time causes fewer privacy leaks\\n(11.75\\\\% vs 12.70\\\\%). Our scheme further outperforms model-based classifiers\\nwhen there are insufficient training data. Since privacy preferences are\\ninnately private, we make our recommender privacy-aware by obfuscating\\npeople's preferences. Our results show that obfuscation leads to a minimal\\nloss of prediction accuracy (0.76\\\\%).},\\n\\turldate = {2015-09-23},\\n\\tbooktitle = {{MOBIQUITOUS} '14},\\n\\tpublisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},\\n\\tauthor = {Zhao, Yuchen and Ye, Juan and Henderson, Tristan},\\n\\tyear = {2014},\\n\\tnote = {Journal Abbreviation: MOBIQUITOUS '14},\\n\\tpages = {120--129},\\n}\\n\\n\",\"author_short\":[\"Zhao, Y.\",\"Ye, J.\",\"Henderson, T.\"],\"key\":\"zhao_privacy-aware_2014\",\"id\":\"zhao_privacy-aware_2014\",\"bibbaseid\":\"zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach\",\"volume\":\"26\",\"issn\":\"1047-7047\",\"url\":\"http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583\",\"doi\":\"10.1287/isre.2015.0583\",\"abstract\":\"Business analytics has evolved from being a novelty used by a select few to an accepted facet of conducting business. Recommender systems form a critical component of the business analytics toolkit and, by enabling firms to effectively target customers with products and services, are helping alter the e-commerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association-rule-based methods being the most common. In this paper, we propose a method to improve the quality of recommendations made using association rules. This is accomplished by combining rules when possible and stands apart from existing rule-combination methods in that it is strongly grounded in probability theory. Combining rules requires the identification of the best combination of rules from the many combinations that might exist, and we use a maximum-likelihood framework to compare alternative combinations. Because it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context—the best solution corresponds to the set of rules that leads to the highest sum of mutual information associated with the rules. Through a variety of experiments that evaluate the quality of recommendations made using the proposed approach, we show that (i) a greedy heuristic used to solve the maximum likelihood estimation problem is very effective, providing results comparable to those from using the optimal set partitioning solution; (ii) the recommendations made by our approach are more accurate than those made by a variety of state-of-the-art benchmarks, including collaborative filtering and matrix factorization; and (iii) the recommendations can be made in a fraction of a second on a desktop computer, making it practical to use in real-world applications.\",\"number\":\"3\",\"urldate\":\"2015-09-19\",\"journal\":\"Information Systems Research\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ghoshal\"],\"firstnames\":[\"Abhijeet\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Menon\"],\"firstnames\":[\"Syam\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sarkar\"],\"firstnames\":[\"Sumit\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2015\",\"pages\":\"532–551\",\"bibtex\":\"@article{ghoshal_recommendations_2015,\\n\\ttitle = {Recommendations {Using} {Information} from {Multiple} {Association} {Rules}: {A} {Probabilistic} {Approach}},\\n\\tvolume = {26},\\n\\tissn = {1047-7047},\\n\\turl = {http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583},\\n\\tdoi = {10.1287/isre.2015.0583},\\n\\tabstract = {Business analytics has evolved from being a novelty used by a select few\\nto an accepted facet of conducting business. Recommender systems form a\\ncritical component of the business analytics toolkit and, by enabling\\nfirms to effectively target customers with products and services, are\\nhelping alter the e-commerce landscape. A variety of methods exist for\\nproviding recommendations, with collaborative filtering, matrix\\nfactorization, and association-rule-based methods being the most common.\\nIn this paper, we propose a method to improve the quality of\\nrecommendations made using association rules. This is accomplished by\\ncombining rules when possible and stands apart from existing\\nrule-combination methods in that it is strongly grounded in probability\\ntheory. Combining rules requires the identification of the best\\ncombination of rules from the many combinations that might exist, and we\\nuse a maximum-likelihood framework to compare alternative combinations.\\nBecause it is impractical to apply the maximum likelihood framework\\ndirectly in real time, we show that this problem can equivalently be\\nrepresented as a set partitioning problem by translating it into an\\ninformation theoretic context—the best solution corresponds to the set of\\nrules that leads to the highest sum of mutual information associated with\\nthe rules. Through a variety of experiments that evaluate the quality of\\nrecommendations made using the proposed approach, we show that (i) a\\ngreedy heuristic used to solve the maximum likelihood estimation problem\\nis very effective, providing results comparable to those from using the\\noptimal set partitioning solution; (ii) the recommendations made by our\\napproach are more accurate than those made by a variety of\\nstate-of-the-art benchmarks, including collaborative filtering and matrix\\nfactorization; and (iii) the recommendations can be made in a fraction of\\na second on a desktop computer, making it practical to use in real-world\\napplications.},\\n\\tnumber = {3},\\n\\turldate = {2015-09-19},\\n\\tjournal = {Information Systems Research},\\n\\tauthor = {Ghoshal, Abhijeet and Menon, Syam and Sarkar, Sumit},\\n\\tmonth = jul,\\n\\tyear = {2015},\\n\\tpages = {532--551},\\n}\\n\\n\",\"author_short\":[\"Ghoshal, A.\",\"Menon, S.\",\"Sarkar, S.\"],\"key\":\"ghoshal_recommendations_2015\",\"id\":\"ghoshal_recommendations_2015\",\"bibbaseid\":\"ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Predicting Human Preferences Using the Block Structure of Complex Social Networks\",\"volume\":\"7\",\"url\":\"http://dx.doi.org/10.1371/journal.pone.0044620\",\"doi\":\"10.1371/journal.pone.0044620\",\"abstract\":\"With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.\",\"number\":\"9\",\"urldate\":\"2014-10-04\",\"journal\":\"PLoS One\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Guimerà\"],\"firstnames\":[\"Roger\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Llorente\"],\"firstnames\":[\"Alejandro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Moro\"],\"firstnames\":[\"Esteban\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sales-Pardo\"],\"firstnames\":[\"Marta\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2012\",\"pages\":\"e44620\",\"bibtex\":\"@article{guimera_predicting_2012,\\n\\ttitle = {Predicting {Human} {Preferences} {Using} the {Block} {Structure} of {Complex} {Social} {Networks}},\\n\\tvolume = {7},\\n\\turl = {http://dx.doi.org/10.1371/journal.pone.0044620},\\n\\tdoi = {10.1371/journal.pone.0044620},\\n\\tabstract = {With ever-increasing available data, predicting individuals' preferences\\nand helping them locate the most relevant information has become a\\npressing need. Understanding and predicting preferences is also important\\nfrom a fundamental point of view, as part of what has been called a “new”\\ncomputational social science. Here, we propose a novel approach based on\\nstochastic block models, which have been developed by sociologists as\\nplausible models of complex networks of social interactions. Our model is\\nin the spirit of predicting individuals' preferences based on the\\npreferences of others but, rather than fitting a particular model, we rely\\non a Bayesian approach that samples over the ensemble of all possible\\nmodels. We show that our approach is considerably more accurate than\\nleading recommender algorithms, with major relative improvements between\\n38\\\\% and 99\\\\% over industry-level algorithms. Besides, our approach sheds\\nlight on decision-making processes by identifying groups of individuals\\nthat have consistently similar preferences, and enabling the analysis of\\nthe characteristics of those groups.},\\n\\tnumber = {9},\\n\\turldate = {2014-10-04},\\n\\tjournal = {PLoS One},\\n\\tauthor = {Guimerà, Roger and Llorente, Alejandro and Moro, Esteban and Sales-Pardo, Marta},\\n\\tmonth = sep,\\n\\tyear = {2012},\\n\\tpages = {e44620},\\n}\\n\\n\",\"author_short\":[\"Guimerà, R.\",\"Llorente, A.\",\"Moro, E.\",\"Sales-Pardo, M.\"],\"key\":\"guimera_predicting_2012\",\"id\":\"guimera_predicting_2012\",\"bibbaseid\":\"guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1371/journal.pone.0044620\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Recommender Systems for the People — Enhancing Personalization in Web Augmentation\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wischenbart\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Firmenich\"],\"firstnames\":[\"Sergio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rossi\"],\"firstnames\":[\"Gustavo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wimmer\"],\"firstnames\":[\"Manuel\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2015\",\"bibtex\":\"@inproceedings{wischenbart_recommender_2015,\\n\\ttitle = {Recommender {Systems} for the {People} — {Enhancing} {Personalization} in {Web} {Augmentation}},\\n\\tauthor = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Wimmer, Manuel},\\n\\tmonth = sep,\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Wischenbart, M.\",\"Firmenich, S.\",\"Rossi, G.\",\"Wimmer, M.\"],\"key\":\"wischenbart_recommender_2015\",\"id\":\"wischenbart_recommender_2015\",\"bibbaseid\":\"wischenbart-firmenich-rossi-wimmer-recommendersystemsforthepeopleenhancingpersonalizationinwebaugmentation-2015\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"BoostMF: Boosted Matrix Factorisation for Collaborative Ranking\",\"isbn\":\"978-3-319-23524-0\",\"url\":\"http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1\",\"urldate\":\"2015-09-19\",\"booktitle\":\"Machine Learning and Knowledge Discovery in Databases\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chowdhury\"],\"firstnames\":[\"Nipa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cai\"],\"firstnames\":[\"Xiongcai\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Luo\"],\"firstnames\":[\"Cheng\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Appice\"],\"firstnames\":[\"Annalisa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rodrigues\"],\"firstnames\":[\"Pedro\",\"Pereira\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Costa\"],\"firstnames\":[\"Vítor\",\"Santos\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gama\"],\"firstnames\":[\"João\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jorge\"],\"firstnames\":[\"Alípio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Soares\"],\"firstnames\":[\"Carlos\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2015\",\"pages\":\"3–18\",\"bibtex\":\"@incollection{chowdhury_boostmf_2015,\\n\\tseries = {Lecture {Notes} in {Computer} {Science}},\\n\\ttitle = {{BoostMF}: {Boosted} {Matrix} {Factorisation} for {Collaborative} {Ranking}},\\n\\tisbn = {978-3-319-23524-0},\\n\\turl = {http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1},\\n\\turldate = {2015-09-19},\\n\\tbooktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Chowdhury, Nipa and Cai, Xiongcai and Luo, Cheng},\\n\\teditor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Costa, Vítor Santos and Gama, João and Jorge, Alípio and Soares, Carlos},\\n\\tmonth = sep,\\n\\tyear = {2015},\\n\\tpages = {3--18},\\n}\\n\\n\",\"author_short\":[\"Chowdhury, N.\",\"Cai, X.\",\"Luo, C.\"],\"editor_short\":[\"Appice, A.\",\"Rodrigues, P. P.\",\"Costa, V. S.\",\"Gama, J.\",\"Jorge, A.\",\"Soares, C.\"],\"key\":\"chowdhury_boostmf_2015\",\"id\":\"chowdhury_boostmf_2015\",\"bibbaseid\":\"chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"Stream-Based Recommendations: Online and Offline Evaluation as a Service\",\"isbn\":\"978-3-319-24026-8\",\"url\":\"http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48\",\"urldate\":\"2015-09-19\",\"booktitle\":\"Experimental IR Meets Multilinguality, Multimodality, and Interaction\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kille\"],\"firstnames\":[\"Benjamin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lommatzsch\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Turrin\"],\"firstnames\":[\"Roberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Serény\"],\"firstnames\":[\"András\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Larson\"],\"firstnames\":[\"Martha\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brodt\"],\"firstnames\":[\"Torben\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Seiler\"],\"firstnames\":[\"Jonas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hopfgartner\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Mothe\"],\"firstnames\":[\"Josiane\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Savoy\"],\"firstnames\":[\"Jacques\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kamps\"],\"firstnames\":[\"Jaap\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pinel-Sauvagnat\"],\"firstnames\":[\"Karen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jones\"],\"firstnames\":[\"Gareth\",\"J\",\"F\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"SanJuan\"],\"firstnames\":[\"Eric\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cappellato\"],\"firstnames\":[\"Linda\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferro\"],\"firstnames\":[\"Nicola\"],\"suffixes\":[]}],\"year\":\"2015\",\"pages\":\"497–517\",\"bibtex\":\"@incollection{kille_stream-based_2015,\\n\\tseries = {Lecture {Notes} in {Computer} {Science}},\\n\\ttitle = {Stream-{Based} {Recommendations}: {Online} and {Offline} {Evaluation} as a {Service}},\\n\\tisbn = {978-3-319-24026-8},\\n\\turl = {http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48},\\n\\turldate = {2015-09-19},\\n\\tbooktitle = {Experimental {IR} {Meets} {Multilinguality}, {Multimodality}, and {Interaction}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Kille, Benjamin and Lommatzsch, Andreas and Turrin, Roberto and Serény, András and Larson, Martha and Brodt, Torben and Seiler, Jonas and Hopfgartner, Frank},\\n\\teditor = {Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, Gareth J F and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola},\\n\\tyear = {2015},\\n\\tpages = {497--517},\\n}\\n\\n\",\"author_short\":[\"Kille, B.\",\"Lommatzsch, A.\",\"Turrin, R.\",\"Serény, A.\",\"Larson, M.\",\"Brodt, T.\",\"Seiler, J.\",\"Hopfgartner, F.\"],\"editor_short\":[\"Mothe, J.\",\"Savoy, J.\",\"Kamps, J.\",\"Pinel-Sauvagnat, K.\",\"Jones, G. J F\",\"SanJuan, E.\",\"Cappellato, L.\",\"Ferro, N.\"],\"key\":\"kille_stream-based_2015\",\"id\":\"kille_stream-based_2015\",\"bibbaseid\":\"kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Gothenburg, Sweden\",\"title\":\"Recommender Systems; Contextual Multi-Armed Bandit Algorithms for the purpose of targeted advertisement within e-commerce\",\"url\":\"http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf\",\"school\":\"Chalmers University of Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ek\"],\"firstnames\":[\"Frederik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stigsson\"],\"firstnames\":[\"Robert\"],\"suffixes\":[]}],\"year\":\"2015\",\"bibtex\":\"@phdthesis{ek_recommender_2015,\\n\\taddress = {Gothenburg, Sweden},\\n\\ttitle = {Recommender {Systems}; {Contextual} {Multi}-{Armed} {Bandit} {Algorithms} for the purpose of targeted advertisement within e-commerce},\\n\\turl = {http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf},\\n\\tschool = {Chalmers University of Technology},\\n\\tauthor = {Ek, Frederik and Stigsson, Robert},\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Ek, F.\",\"Stigsson, R.\"],\"key\":\"ek_recommender_2015\",\"id\":\"ek_recommender_2015\",\"bibbaseid\":\"ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine\",\"issn\":\"0219-1377\",\"url\":\"http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z\",\"doi\":\"10.1007/s10115-015-0866-z\",\"urldate\":\"2015-09-19\",\"journal\":\"Knowl. Inf. Syst.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Christou\"],\"firstnames\":[\"Ioannis\",\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Amolochitis\"],\"firstnames\":[\"Emmanouil\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tan\"],\"firstnames\":[\"Zheng-Hua\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2015\",\"pages\":\"1–26\",\"bibtex\":\"@article{christou_amore_2015,\\n\\ttitle = {{AMORE}: design and implementation of a commercial-strength parallel hybrid movie recommendation engine},\\n\\tissn = {0219-1377},\\n\\turl = {http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z},\\n\\tdoi = {10.1007/s10115-015-0866-z},\\n\\turldate = {2015-09-19},\\n\\tjournal = {Knowl. Inf. Syst.},\\n\\tauthor = {Christou, Ioannis T and Amolochitis, Emmanouil and Tan, Zheng-Hua},\\n\\tmonth = aug,\\n\\tyear = {2015},\\n\\tpages = {1--26},\\n}\\n\\n\",\"author_short\":[\"Christou, I. T\",\"Amolochitis, E.\",\"Tan, Z.\"],\"key\":\"christou_amore_2015\",\"id\":\"christou_amore_2015\",\"bibbaseid\":\"christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Towards Making Systems Forget with Machine Unlearning\",\"url\":\"http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf\",\"abstract\":\"Today’s systems produce a wealth of data every day, and the data further generates more data, i.e., the derived data, forming into a complex data propagation network, defined as the data’s lineage. There are many reasons for users and administrators to forget certain data including the data’s lineage. From the privacy perspective, a system may leak private information of certain users, and those users unhappy about privacy leaks naturally want to forget their data and its lineage. From the security perspective, an anomaly detection system can be polluted by adversaries through injecting manually crafted data into the training set. Therefore, we envision forgetting systems, capable of completely forgetting certain data and its lineage. In this paper, we focus on making learning systems forget, the process of which is defined as machine unlearning or unlearning. To perform unlearning upon learning system, we present general unlearning criteria, i.e., converting a learning system or part of it into a summation form of statistical query learning model, and updating all the summations to achieve unlearning. Then, we integrate our unlearning criteria into an unlearning architecture that interacts with all the components of a learning system, such as sample clustering and feature selection. To demonstrate our unlearning criteria and architecture, we select four real-world learning systems, including an item-item recommendation system, an online social network spam filter, and a malware detection system. These systems are first exposed to an adversarial environment, e.g., if the system is potentially vulnerable to training data pollution, we first pollute the training data set and show that the detection rate drops significantly. Then, we apply our unlearning technique upon those affected systems, either polluted or leaking private information. Our results show that after unlearning, the detection rate of a polluted system increases back to the one before pollution, and a system leaking a particular user’s private information completely forgets that information.\",\"publisher\":\"IEEE\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Cao\"],\"firstnames\":[\"Yinzhi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Junfeng\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2015\",\"bibtex\":\"@inproceedings{cao_towards_2015,\\n\\ttitle = {Towards {Making} {Systems} {Forget} with {Machine} {Unlearning}},\\n\\turl = {http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf},\\n\\tabstract = {Today’s systems produce a wealth of data every day, and the data further\\ngenerates more data, i.e., the derived data, forming into a complex data\\npropagation network, defined as the data’s lineage. There are many reasons\\nfor users and administrators to forget certain data including the data’s\\nlineage. From the privacy perspective, a system may leak private\\ninformation of certain users, and those users unhappy about privacy leaks\\nnaturally want to forget their data and its lineage. From the security\\nperspective, an anomaly detection system can be polluted by adversaries\\nthrough injecting manually crafted data into the training set. Therefore,\\nwe envision forgetting systems, capable of completely forgetting certain\\ndata and its lineage. In this paper, we focus on making learning systems\\nforget, the process of which is defined as machine unlearning or\\nunlearning. To perform unlearning upon learning system, we present general\\nunlearning criteria, i.e., converting a learning system or part of it into\\na summation form of statistical query learning model, and updating all the\\nsummations to achieve unlearning. Then, we integrate our unlearning\\ncriteria into an unlearning architecture that interacts with all the\\ncomponents of a learning system, such as sample clustering and feature\\nselection. To demonstrate our unlearning criteria and architecture, we\\nselect four real-world learning systems, including an item-item\\nrecommendation system, an online social network spam filter, and a malware\\ndetection system. These systems are first exposed to an adversarial\\nenvironment, e.g., if the system is potentially vulnerable to training\\ndata pollution, we first pollute the training data set and show that the\\ndetection rate drops significantly. Then, we apply our unlearning\\ntechnique upon those affected systems, either polluted or leaking private\\ninformation. Our results show that after unlearning, the detection rate of\\na polluted system increases back to the one before pollution, and a system\\nleaking a particular user’s private information completely forgets that\\ninformation.},\\n\\tpublisher = {IEEE},\\n\\tauthor = {Cao, Yinzhi and Yang, Junfeng},\\n\\tmonth = may,\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Cao, Y.\",\"Yang, J.\"],\"key\":\"cao_towards_2015\",\"id\":\"cao_towards_2015\",\"bibbaseid\":\"cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine\",\"volume\":\"29\",\"issn\":\"1541-1672\",\"url\":\"http://dx.doi.org/10.1109/MIS.2014.23\",\"doi\":\"10.1109/MIS.2014.23\",\"abstract\":\"AMORE is a hybrid recommendation system that provides movie recommendations for a major triple-play services provider in Greece. Combined with our own implementations of several user-, item-, and content-based recommendation algorithms, AMORE significantly outperforms other state-of-the-art implementations both in solution quality and response time. AMORE currently serves daily recommendation requests for all active subscribers of the provider's video-on-demand services and has contributed to an increase of rental profits and customer retention.\",\"number\":\"2\",\"journal\":\"IEEE Intell. Syst.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Amolochitis\"],\"firstnames\":[\"E\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Christou\"],\"firstnames\":[\"I\",\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tan\"],\"firstnames\":[\"Zheng-Hua\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2014\",\"pages\":\"92–96\",\"bibtex\":\"@article{amolochitis_implementing_2014,\\n\\ttitle = {Implementing a {Commercial}-{Strength} {Parallel} {Hybrid} {Movie} {Recommendation} {Engine}},\\n\\tvolume = {29},\\n\\tissn = {1541-1672},\\n\\turl = {http://dx.doi.org/10.1109/MIS.2014.23},\\n\\tdoi = {10.1109/MIS.2014.23},\\n\\tabstract = {AMORE is a hybrid recommendation system that provides movie\\nrecommendations for a major triple-play services provider in Greece.\\nCombined with our own implementations of several user-, item-, and\\ncontent-based recommendation algorithms, AMORE significantly outperforms\\nother state-of-the-art implementations both in solution quality and\\nresponse time. AMORE currently serves daily recommendation requests for\\nall active subscribers of the provider's video-on-demand services and has\\ncontributed to an increase of rental profits and customer retention.},\\n\\tnumber = {2},\\n\\tjournal = {IEEE Intell. Syst.},\\n\\tauthor = {Amolochitis, E and Christou, I T and Tan, Zheng-Hua},\\n\\tmonth = mar,\\n\\tyear = {2014},\\n\\tpages = {92--96},\\n}\\n\\n\",\"author_short\":[\"Amolochitis, E\",\"Christou, I T\",\"Tan, Z.\"],\"key\":\"amolochitis_implementing_2014\",\"id\":\"amolochitis_implementing_2014\",\"bibbaseid\":\"amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1109/MIS.2014.23\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Recommendation Systems Based on Online User's Action\",\"url\":\"http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69\",\"doi\":\"10.1109/CIT/IUCC/DASC/PICOM.2015.69\",\"abstract\":\"In this paper, we propose a new recommender algorithm based on multi-dimensional users behavior and new measurements. It's used in the framework of our recommender system that use knowledge discovery techniques to the problem of making product recommendations during a live user interaction. Most of Collaborative filtering algorithms based on user's rating or similar item that other users bought, we propose to combine all user's action to predict recommendation. These systems are achieving widespread success in E-tourism nowadays. We evaluate our algorithm on tourism dataset. Evaluations have shown good results. We compared our algorithm to Slope One and Weight Slope One. We obtained an improvement of 5% in precision and recall. And an improvement of 12% in RMSE and nDCG.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Elkhelifi\"],\"firstnames\":[\"A\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kharrat\"],\"firstnames\":[\"F\",\"Ben\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Faiz\"],\"firstnames\":[\"R\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2015\",\"pages\":\"485–490\",\"bibtex\":\"@inproceedings{elkhelifi_recommendation_2015,\\n\\ttitle = {Recommendation {Systems} {Based} on {Online} {User}'s {Action}},\\n\\turl = {http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69},\\n\\tdoi = {10.1109/CIT/IUCC/DASC/PICOM.2015.69},\\n\\tabstract = {In this paper, we propose a new recommender algorithm based on\\nmulti-dimensional users behavior and new measurements. It's used in the\\nframework of our recommender system that use knowledge discovery\\ntechniques to the problem of making product recommendations during a live\\nuser interaction. Most of Collaborative filtering algorithms based on\\nuser's rating or similar item that other users bought, we propose to\\ncombine all user's action to predict recommendation. These systems are\\nachieving widespread success in E-tourism nowadays. We evaluate our\\nalgorithm on tourism dataset. Evaluations have shown good results. We\\ncompared our algorithm to Slope One and Weight Slope One. We obtained an\\nimprovement of 5\\\\% in precision and recall. And an improvement of 12\\\\% in\\nRMSE and nDCG.},\\n\\tauthor = {Elkhelifi, A and Kharrat, F Ben and Faiz, R},\\n\\tmonth = oct,\\n\\tyear = {2015},\\n\\tpages = {485--490},\\n}\\n\\n\",\"author_short\":[\"Elkhelifi, A\",\"Kharrat, F B.\",\"Faiz, R\"],\"key\":\"elkhelifi_recommendation_2015\",\"id\":\"elkhelifi_recommendation_2015\",\"bibbaseid\":\"elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Stockholm, Sweden\",\"title\":\"Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems\",\"url\":\"http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=1&af=%5B%5D&searchType=LIST_LATEST&query=&language=en&pid=diva2%3A927356&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-7195\",\"abstract\":\"Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two ...\",\"urldate\":\"2016-06-13\",\"school\":\"KTH Royal Institute of Technology\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Salam\",\"Patrous\"],\"firstnames\":[\"Ziad\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Najafi\"],\"firstnames\":[\"Safir\"],\"suffixes\":[]}],\"year\":\"2016\",\"bibtex\":\"@phdthesis{salam_patrous_evaluating_2016,\\n\\taddress = {Stockholm, Sweden},\\n\\ttitle = {Evaluating {Prediction} {Accuracy} for {Collaborative} {Filtering} {Algorithms} in {Recommender} {Systems}},\\n\\turl = {http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=1&af=%5B%5D&searchType=LIST_LATEST&query=&language=en&pid=diva2%3A927356&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-7195},\\n\\tabstract = {Recommender systems are a relatively new technology that is commonly used\\nby e-commerce websites and streaming services among others, to predict\\nuser opinion about products. This report studies two ...},\\n\\turldate = {2016-06-13},\\n\\tschool = {KTH Royal Institute of Technology},\\n\\tauthor = {Salam Patrous, Ziad and Najafi, Safir},\\n\\tyear = {2016},\\n}\\n\\n\",\"author_short\":[\"Salam Patrous, Z.\",\"Najafi, S.\"],\"key\":\"salam_patrous_evaluating_2016\",\"id\":\"salam_patrous_evaluating_2016\",\"bibbaseid\":\"salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=1&af=%5B%5D&searchType=LIST_LATEST&query=&language=en&pid=diva2%3A927356&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-7195\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Minneapolis, MN, USA\",\"title\":\"Leveraging Collective Intelligence in Recommender System\",\"url\":\"http://hdl.handle.net/11299/182725\",\"abstract\":\"Recommender systems, since their introduction 20 years ago, have been widely deployed in web services to alleviate user information overload. Driven by business objectives of their applications, the focus of recommender systems has shifted from accurately modeling and predicting user preferences to offering good personalized user experience. The later is difficult because there are many factors, e.g., tenure of a user, context of recommendation and transparency of recommender system, that affect users' perception of recommendations. Many of these factors are subjective and not easily quantifiable, posing challenges to recommender algorithms. When pure algorithmic solutions are at their limits in providing good user experience in recommender systems, we turn to the collective intelligence of human and computer. Computer and human are complementary to each other: computers are fast at computation and data processing and have accurate memory; humans are capable of complex reasoning, being creative and relating to other humans. In fact, such close collaborations between human and computer have precedent: after chess master Garry Kasparov lost to IBM computer ``Deep Blue'', he invited a new form of chess — advanced chess, in which human player and a computer program teams up against such pairs. In this thesis, we leverage the collective intelligence of human and computer to tackle several challenges in recommender systems and demonstrate designs of such hybrid systems. We make contributions to the following aspects of recommender systems: providing better new user experience, enhancing topic modeling component for items, composing better recommendation sets and generating personalized natural language explanations. These four applications demonstrate different ways of designing systems with collective intelligence, applicable to domains other than recommender systems. We believe the collective intelligence of human and computer can power more intelligent, user friendly and creative systems, worthy of continuous research effort in future.\",\"urldate\":\"2016-11-01\",\"school\":\"University of Minnesota\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chang\"],\"firstnames\":[\"Shuo\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2016\",\"bibtex\":\"@phdthesis{chang_leveraging_2016,\\n\\taddress = {Minneapolis, MN, USA},\\n\\ttitle = {Leveraging {Collective} {Intelligence} in {Recommender} {System}},\\n\\turl = {http://hdl.handle.net/11299/182725},\\n\\tabstract = {Recommender systems, since their introduction 20 years ago, have been\\nwidely deployed in web services to alleviate user information overload.\\nDriven by business objectives of their applications, the focus of\\nrecommender systems has shifted from accurately modeling and predicting\\nuser preferences to offering good personalized user experience. The later\\nis difficult because there are many factors, e.g., tenure of a user,\\ncontext of recommendation and transparency of recommender system, that\\naffect users' perception of recommendations. Many of these factors are\\nsubjective and not easily quantifiable, posing challenges to recommender\\nalgorithms. When pure algorithmic solutions are at their limits in\\nproviding good user experience in recommender systems, we turn to the\\ncollective intelligence of human and computer. Computer and human are\\ncomplementary to each other: computers are fast at computation and data\\nprocessing and have accurate memory; humans are capable of complex\\nreasoning, being creative and relating to other humans. In fact, such\\nclose collaborations between human and computer have precedent: after\\nchess master Garry Kasparov lost to IBM computer ``Deep Blue'', he invited\\na new form of chess --- advanced chess, in which human player and a\\ncomputer program teams up against such pairs. In this thesis, we leverage\\nthe collective intelligence of human and computer to tackle several\\nchallenges in recommender systems and demonstrate designs of such hybrid\\nsystems. We make contributions to the following aspects of recommender\\nsystems: providing better new user experience, enhancing topic modeling\\ncomponent for items, composing better recommendation sets and generating\\npersonalized natural language explanations. These four applications\\ndemonstrate different ways of designing systems with collective\\nintelligence, applicable to domains other than recommender systems. We\\nbelieve the collective intelligence of human and computer can power more\\nintelligent, user friendly and creative systems, worthy of continuous\\nresearch effort in future.},\\n\\turldate = {2016-11-01},\\n\\tschool = {University of Minnesota},\\n\\tauthor = {Chang, Shuo},\\n\\tmonth = aug,\\n\\tyear = {2016},\\n}\\n\\n\",\"author_short\":[\"Chang, S.\"],\"key\":\"chang_leveraging_2016\",\"id\":\"chang_leveraging_2016\",\"bibbaseid\":\"chang-leveragingcollectiveintelligenceinrecommendersystem-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://hdl.handle.net/11299/182725\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Hybrid group recommendations for a travel service\",\"volume\":\"75\",\"issn\":\"1380-7501\",\"url\":\"http://link.springer.com/article/10.1007/s11042-016-3265-x\",\"doi\":\"10.1007/s11042-016-3265-x\",\"abstract\":\"Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users’ rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.\",\"number\":\"5\",\"urldate\":\"2016-03-11\",\"journal\":\"Multimed. Tools Appl.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pessemier\"],\"firstnames\":[\"Toon\",\"De\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dhondt\"],\"firstnames\":[\"Jeroen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Martens\"],\"firstnames\":[\"Luc\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2016\",\"pages\":\"1–25\",\"bibtex\":\"@article{pessemier_hybrid_2016,\\n\\ttitle = {Hybrid group recommendations for a travel service},\\n\\tvolume = {75},\\n\\tissn = {1380-7501},\\n\\turl = {http://link.springer.com/article/10.1007/s11042-016-3265-x},\\n\\tdoi = {10.1007/s11042-016-3265-x},\\n\\tabstract = {Recommendation techniques have proven their usefulness as a tool to cope\\nwith the information overload problem in many classical domains such as\\nmovies, books, and music. Additional challenges for recommender systems\\nemerge in the domain of tourism such as acquiring metadata and feedback,\\nthe sparsity of the rating matrix, user constraints, and the fact that\\ntraveling is often a group activity. This paper proposes a recommender\\nsystem that offers personalized recommendations for travel destinations to\\nindividuals and groups. These recommendations are based on the users’\\nrating profile, personal interests, and specific demands for their next\\ndestination. The recommendation algorithm is a hybrid approach combining a\\ncontent-based, collaborative filtering, and knowledge-based solution. For\\ngroups of users, such as families or friends, individual recommendations\\nare aggregated into group recommendations, with an additional opportunity\\nfor users to give feedback on these group recommendations. A group of test\\nusers evaluated the recommender system using a prototype web application.\\nThe results prove the usefulness of individual and group recommendations\\nand show that users prefer the hybrid algorithm over each individual\\ntechnique. This paper demonstrates the added value of various\\nrecommendation algorithms in terms of different quality aspects, compared\\nto an unpersonalized list of the most-popular destinations.},\\n\\tnumber = {5},\\n\\turldate = {2016-03-11},\\n\\tjournal = {Multimed. Tools Appl.},\\n\\tauthor = {Pessemier, Toon De and Dhondt, Jeroen and Martens, Luc},\\n\\tmonth = jan,\\n\\tyear = {2016},\\n\\tpages = {1--25},\\n}\\n\\n\",\"author_short\":[\"Pessemier, T. D.\",\"Dhondt, J.\",\"Martens, L.\"],\"key\":\"pessemier_hybrid_2016\",\"id\":\"pessemier_hybrid_2016\",\"bibbaseid\":\"pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://link.springer.com/article/10.1007/s11042-016-3265-x\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Minneapolis, MN, USA\",\"title\":\"Enhancing User Experience With Recommender Systems Beyond Prediction Accuracies\",\"url\":\"http://hdl.handle.net/11299/182780\",\"abstract\":\"In this dissertation, we examine to improve the user experience with recommender systems beyond prediction accuracy. We focus on the following aspects of the user experience. In chapter 3 we examine if a recommender system exposes users to less diverse contents over time. In chapter 4 we look at the relationships between user personality and user preferences for recommendation diversity, popularity, and serendipity. In chapter 5 we investigate the relations between the self-reported user satisfaction and the three recommendation properties with the inferred user recommendation consumption. In chapter 6 we look at four different rating inter- faces and evaluated how these interfaces affected the user rating experience. We find that over time a recommender system exposes users to less-diverse contents and that users rate less-diverse items. However, users who took recommendations were exposed to more diverse recommendations than those who did not. Furthermore, users with different personalities have different preferences for recommendation diversity, popularity, and serendipity (e.g. some users prefer more diverse recommendations, while others prefer similar ones). We also find that user satisfaction with recommendation popularity and serendipity measured with survey questions strongly relate to user recommendation consumption inferred with logged data. We then propose a way to get better signals about user preferences and help users rate items in the recommendation systems more consistently. That is providing exemplars to users at the time they rate the items improved the consistency of users’ ratings. Our results suggest several ways recommender system practitioners and re- searchers can enrich the user experience. For example, by integrating users’ personality into recommendation frameworks, we can help recommender systems deliver recommendations with the preferred levels of diversity, popularity, and serendipity to individual users. We can also facilitate the rating process by integrating a set of proven rating-support techniques into the systems’ interfaces.\",\"urldate\":\"2016-11-01\",\"school\":\"University of Minnesota\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Nguyen\"],\"firstnames\":[\"Tien\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2016\",\"bibtex\":\"@phdthesis{nguyen_enhancing_2016,\\n\\taddress = {Minneapolis, MN, USA},\\n\\ttitle = {Enhancing {User} {Experience} {With} {Recommender} {Systems} {Beyond} {Prediction} {Accuracies}},\\n\\turl = {http://hdl.handle.net/11299/182780},\\n\\tabstract = {In this dissertation, we examine to improve the user experience with\\nrecommender systems beyond prediction accuracy. We focus on the following\\naspects of the user experience. In chapter 3 we examine if a recommender\\nsystem exposes users to less diverse contents over time. In chapter 4 we\\nlook at the relationships between user personality and user preferences\\nfor recommendation diversity, popularity, and serendipity. In chapter 5 we\\ninvestigate the relations between the self-reported user satisfaction and\\nthe three recommendation properties with the inferred user recommendation\\nconsumption. In chapter 6 we look at four different rating inter- faces\\nand evaluated how these interfaces affected the user rating experience. We\\nfind that over time a recommender system exposes users to less-diverse\\ncontents and that users rate less-diverse items. However, users who took\\nrecommendations were exposed to more diverse recommendations than those\\nwho did not. Furthermore, users with different personalities have\\ndifferent preferences for recommendation diversity, popularity, and\\nserendipity (e.g. some users prefer more diverse recommendations, while\\nothers prefer similar ones). We also find that user satisfaction with\\nrecommendation popularity and serendipity measured with survey questions\\nstrongly relate to user recommendation consumption inferred with logged\\ndata. We then propose a way to get better signals about user preferences\\nand help users rate items in the recommendation systems more consistently.\\nThat is providing exemplars to users at the time they rate the items\\nimproved the consistency of users’ ratings. Our results suggest several\\nways recommender system practitioners and re- searchers can enrich the\\nuser experience. For example, by integrating users’ personality into\\nrecommendation frameworks, we can help recommender systems deliver\\nrecommendations with the preferred levels of diversity, popularity, and\\nserendipity to individual users. We can also facilitate the rating process\\nby integrating a set of proven rating-support techniques into the systems’\\ninterfaces.},\\n\\turldate = {2016-11-01},\\n\\tschool = {University of Minnesota},\\n\\tauthor = {Nguyen, Tien},\\n\\tmonth = aug,\\n\\tyear = {2016},\\n}\\n\\n\",\"author_short\":[\"Nguyen, T.\"],\"key\":\"nguyen_enhancing_2016\",\"id\":\"nguyen_enhancing_2016\",\"bibbaseid\":\"nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://hdl.handle.net/11299/182780\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Technical Report on evaluation of recommendations generated by spreading activation\",\"url\":\"http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Benjamin\",\"Heitmann\"],\"firstnames\":[\"Conor\",\"Hayes\"],\"suffixes\":[]}],\"year\":\"2013\",\"bibtex\":\"@article{benjamin_heitmann_technical_2013,\\n\\ttitle = {Technical {Report} on evaluation of recommendations generated by spreading activation},\\n\\turl = {http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation},\\n\\tauthor = {Benjamin Heitmann, Conor Hayes},\\n\\tyear = {2013},\\n}\\n\\n\",\"author_short\":[\"Benjamin Heitmann, C. H.\"],\"key\":\"benjamin_heitmann_technical_2013\",\"id\":\"benjamin_heitmann_technical_2013\",\"bibbaseid\":\"benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Exploiting Reviews to Guide Users’ Selections\",\"url\":\"http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf\",\"urldate\":\"2017-03-01\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Dragovic\"],\"firstnames\":[\"Nevena\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pera\"],\"firstnames\":[\"Maria\",\"Soledad\"],\"suffixes\":[]}],\"year\":\"2015\",\"bibtex\":\"@inproceedings{dragovic_exploiting_2015,\\n\\ttitle = {Exploiting {Reviews} to {Guide} {Users}’ {Selections}},\\n\\turl = {http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf},\\n\\turldate = {2017-03-01},\\n\\tauthor = {Dragovic, Nevena and Pera, Maria Soledad},\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Dragovic, N.\",\"Pera, M. S.\"],\"key\":\"dragovic_exploiting_2015\",\"id\":\"dragovic_exploiting_2015\",\"bibbaseid\":\"dragovic-pera-exploitingreviewstoguideusersselections-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Towards Improving Top-N Recommendation by Generalization of SLIM\",\"url\":\"http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Larraín\"],\"firstnames\":[\"Santiago\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Parra\"],\"firstnames\":[\"Denis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Soto\"],\"firstnames\":[\"Alvaro\"],\"suffixes\":[]}],\"year\":\"2015\",\"bibtex\":\"@inproceedings{larrain_towards_2015,\\n\\ttitle = {Towards {Improving} {Top}-{N} {Recommendation} by {Generalization} of {SLIM}},\\n\\turl = {http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf},\\n\\tauthor = {Larraín, Santiago and Parra, Denis and Soto, Alvaro},\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Larraín, S.\",\"Parra, D.\",\"Soto, A.\"],\"key\":\"larrain_towards_2015\",\"id\":\"larrain_towards_2015\",\"bibbaseid\":\"larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"phdthesis\",\"type\":\"phdthesis\",\"address\":\"Gent, Belgium\",\"title\":\"A hybrid group recommender system for travel destinations\",\"school\":\"University of Gent\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Dhondt\"],\"firstnames\":[\"Jeroen\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2015\",\"bibtex\":\"@phdthesis{dhondt_hybrid_2015,\\n\\taddress = {Gent, Belgium},\\n\\ttitle = {A hybrid group recommender system for travel destinations},\\n\\tschool = {University of Gent},\\n\\tauthor = {Dhondt, Jeroen},\\n\\tmonth = may,\\n\\tyear = {2015},\\n}\\n\\n\",\"author_short\":[\"Dhondt, J.\"],\"key\":\"dhondt_hybrid_2015\",\"id\":\"dhondt_hybrid_2015\",\"bibbaseid\":\"dhondt-ahybridgrouprecommendersystemfortraveldestinations-2015\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"title\":\"Machine ‘Unlearning’ Technique Wipes Out Unwanted Data Quickly and Completely\",\"url\":\"http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely\",\"abstract\":\"Cao and Yang believe that easy adoption of forgetting systems will be increasingly in demand. The pair has developed a way to do it faster and more effectively than what is currently available. Their concept, called \\\"machine unlearning,\\\" is so promising that the duo have been awarded a four-year, $1.2 million National Science Foundation grant — split between Lehigh and Columbia — to develop the approach.\",\"urldate\":\"2016-03-16\",\"month\":\"March\",\"year\":\"2016\",\"bibtex\":\"@misc{noauthor_machine_2016,\\n\\ttitle = {Machine ‘{Unlearning}’ {Technique} {Wipes} {Out} {Unwanted} {Data} {Quickly} and {Completely}},\\n\\turl = {http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely},\\n\\tabstract = {Cao and Yang believe that easy adoption of forgetting systems will be\\nincreasingly in demand. The pair has developed a way to do it faster and\\nmore effectively than what is currently available. Their concept, called\\n\\\"machine unlearning,\\\" is so promising that the duo have been awarded a\\nfour-year, \\\\$1.2 million National Science Foundation grant — split between\\nLehigh and Columbia — to develop the approach.},\\n\\turldate = {2016-03-16},\\n\\tmonth = mar,\\n\\tyear = {2016},\\n}\\n\\n\",\"key\":\"noauthor_machine_2016\",\"id\":\"noauthor_machine_2016\",\"bibbaseid\":\"anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016\",\"role\":\"\",\"urls\":{\"Paper\":\"http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Heart rate monitoring, activity recognition, and recommendation for e-coaching\",\"issn\":\"1380-7501\",\"url\":\"https://link.springer.com/article/10.1007/s11042-018-5640-2\",\"doi\":\"10.1007/s11042-018-5640-2\",\"abstract\":\"Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter.\",\"urldate\":\"2018-02-08\",\"journal\":\"Multimed. Tools Appl.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"De\",\"Pessemier\"],\"firstnames\":[\"Toon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Martens\"],\"firstnames\":[\"Luc\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2018\",\"note\":\"Publisher: Springer US\",\"pages\":\"1–18\",\"bibtex\":\"@article{de_pessemier_heart_2018,\\n\\ttitle = {Heart rate monitoring, activity recognition, and recommendation for e-coaching},\\n\\tissn = {1380-7501},\\n\\turl = {https://link.springer.com/article/10.1007/s11042-018-5640-2},\\n\\tdoi = {10.1007/s11042-018-5640-2},\\n\\tabstract = {Equipped with hardware, such as accelerometer and heart rate sensor,\\nwearables enable measuring physical activities and heart rate. However,\\nthe accuracy of these heart rate measurements is still unclear and the\\ncoupling with activity recognition is often missing in health apps. This\\nstudy evaluates heart rate monitoring with four different device types: a\\nspecialized sports device with chest strap, a fitness tracker, a smart\\nwatch, and a smartphone using photoplethysmography. In a state of rest,\\nsimilar measurement results are obtained with the four devices. During\\nphysical activities, the fitness tracker, smart watch, and smartphone\\nmeasure sudden variations in heart rate with a delay, due to movements of\\nthe wrist. Moreover, this study showed that physical activities, such as\\nsquats and dumbbell curl, can be recognized with fitness trackers. By\\ncombining heart rate monitoring and activity recognition, personal\\nsuggestions for physical activities are generated using a tag-based\\nrecommender and rule-based filter.},\\n\\turldate = {2018-02-08},\\n\\tjournal = {Multimed. Tools Appl.},\\n\\tauthor = {De Pessemier, Toon and Martens, Luc},\\n\\tmonth = jan,\\n\\tyear = {2018},\\n\\tnote = {Publisher: Springer US},\\n\\tpages = {1--18},\\n}\\n\",\"author_short\":[\"De Pessemier, T.\",\"Martens, L.\"],\"key\":\"de_pessemier_heart_2018\",\"id\":\"de_pessemier_heart_2018\",\"bibbaseid\":\"depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/article/10.1007/s11042-018-5640-2\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"items\" href=\"data:text/bibtex;base64,@article{harper_movielens_2015,
	title = {The {MovieLens} {Datasets}: {History} and {Context}},
	volume = {5},
	issn = {2160-6455},
	url = {http://doi.acm.org/10.1145/2827872},
	doi = {10.1145/2827872},
	abstract = {The MovieLens datasets are widely used in education, research, and
industry. They are downloaded hundreds of thousands of times each year,
reflecting their use in popular press programming books, traditional and
online courses, and software. These datasets are a product of member
activity in the MovieLens movie recommendation system, an active research
platform that has hosted many experiments since its launch in 1997. This
article documents the history of MovieLens and the MovieLens datasets. We
include a discussion of lessons learned from running a long-standing, live
research platform from the perspective of a research organization. We
document best practices and limitations of using the MovieLens datasets in
new research.},
	number = {4},
	urldate = {2016-03-11},
	journal = {ACM Transactions on Interactive Intelligent Systems},
	author = {Harper, F Maxwell and Konstan, Joseph A},
	month = dec,
	year = {2015},
	keywords = {dataset},
	pages = {19:1--19:19},
}



@inproceedings{ekstrand_all_2018,
	series = {Proceedings of {Machine} {Learning} {Research}},
	title = {All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness},
	volume = {81},
	url = {https://proceedings.mlr.press/v81/ekstrand18b.html},
	abstract = {In the research literature, evaluations of recommender system
effectiveness typically report results over a given data set, providing an
aggregate measure of effectiveness over each instance (e.g. user) in the
data set. Recent advances in information retrieval evaluation, however,
demonstrate the importance of considering the distribution of
effectiveness across diverse groups of varying sizes. For example, do
users of different ages or genders obtain similar utility from the system,
particularly if their group is a relatively small subset of the user base?
We apply this consideration to recommender systems, using offline
evaluation and a utility-based metric of recommendation effectiveness to
explore whether different user demographic groups experience similar
recommendation accuracy. We find demographic differences in measured
recommender effectiveness across two data sets containing different types
of feedback in different domains; these differences sometimes, but not
always, correlate with the size of the user group in question. Demographic
effects also have a complex—and likely detrimental—interaction with
popularity bias, a known deficiency of recommender evaluation. These
results demonstrate the need for recommender system evaluation protocols
that explicitly quantify the degree to which the system is meeting the
information needs of all its users, as well as the need for researchers
and operators to move beyond naïve evaluations that favor the needs of
larger subsets of the user population while ignoring smaller subsets.},
	booktitle = {Proceedings of the 1st {Conference} on {Fairness}, {Accountability} and {Transparency}},
	publisher = {PMLR},
	author = {Ekstrand, Michael D and Tian, Mucun and Azpiazu, Ion Madrazo and Ekstrand, Jennifer D and Anuyah, Oghenemaro and McNeill, David and Pera, Maria Soledad},
	editor = {Friedler, Sorelle A and Wilson, Christo},
	year = {2018},
	note = {Journal Abbreviation: Proceedings of Machine Learning Research},
	pages = {172--186},
}



@inproceedings{ekstrand_sturgeon_2017,
	series = {{FLAIRS} 30},
	title = {Sturgeon and the {Cool} {Kids}: {Problems} with {Top}-{N} {Recommender} {Evaluation}},
	url = {https://aaai.org/papers/639-flairs-2017-15534/},
	abstract = {Top-N evaluation of recommender systems, typically carried out using
metrics from information retrieval or machine learning, has several
challenges. Two of these challenges are popularity bias, where the
evaluation intrinsically favors algorithms that recommend popular items,
and misclassified decoys, where items for which no user relevance is known
are actually relevant to the user, but the evaluation is unaware and
penalizes the recommender for suggesting them. One strategy for mitigating
the misclassified decoy problem is the one-plus-random evaluation strategy
and its generalization, which we call random decoys. In this work, we
explore the random decoy strategy through both a theoretical treatment and
an empirical study, but find little evidence to guide its tuning and show
that it has complex and deleterious interactions with popularity bias.},
	booktitle = {Proceedings of the 30th {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}},
	publisher = {AAAI Press},
	author = {Ekstrand, Michael D and Mahant, Vaibhav},
	month = may,
	year = {2017},
}



@inproceedings{channamsetty_recommender_2017,
	title = {Recommender response to diversity and popularity bias in user profiles},
	url = {https://aaai.org/papers/657-flairs-2017-15524/},
	abstract = {Recommender system evaluation usually focuses on the overall effectiveness
of the algorithms, either in terms of measurable accuracy or ability to
deliver user satisfaction or improve business metrics. When additional
factors are considered, such as the diversity or novelty of the
recommendations, the focus typically remains on the algorithm’s overall
performance. We examine the relationship of the recommender’s output
characteristics – accuracy, popularity (as an inverse of novelty), and
diversity – to characteristics of the user’s rating profile. The aims of
this analysis are twofold: (1) to probe the conditions under which common
algorithms produce more or less diverse or popular recommendations, and
(2) to determine if these personalized recommender algorithms reflect a
user’s preference for diversity or novelty. We trained recommenders on the
MovieLens data and looked for correlation between the user profile and the
recommender’s output for both diversity and popularity bias using
different metrics. We find that the diversity and popularity of movies in
users’ profiles has little impact on the recommendations they receive.},
	urldate = {2017-05-29},
	booktitle = {Proceedings of the 30th {Florida} artificial intelligence research society conference},
	publisher = {AAAI Press},
	author = {Channamsetty, Sushma and Ekstrand, Michael D},
	month = may,
	year = {2017},
}



@inproceedings{ekstrand_exploring_2018,
	address = {New York, NY, USA},
	title = {Exploring author gender in book rating and recommendation},
	url = {https://dl.acm.org/doi/10.1145/3240323.3240373},
	doi = {10.1145/3240323.3240373},
	abstract = {Collaborative filtering algorithms find useful patterns in rating and
consumption data and exploit these patterns to guide users to good items.
Many of the patterns in rating datasets reflect important real-world
differences between the various users and items in the data; other
patterns may be irrelevant or possibly undesirable for social or ethical
reasons, particularly if they reflect undesired discrimination, such as
gender or ethnic discrimination in publishing. In this work, we examine
the response of collaborative filtering recommender algorithms to the
distribution of their input data with respect to a dimension of social
concern, namely content creator gender. Using publicly-available book
ratings data, we measure the distribution of the genders of the authors of
books in user rating profiles and recommendation lists produced from this
data. We find that common collaborative filtering algorithms differ in the
gender distribution of their recommendation lists, and in the relationship
of that output distribution to user profile distribution.},
	publisher = {ACM},
	author = {Ekstrand, Michael D and Tian, Mucun and Kazi, Mohammed R Imran and Mehrpouyan, Hoda and Kluver, Daniel},
	month = sep,
	year = {2018},
}



@inproceedings{dragovic_recommendation_2018,
	title = {From recommendation to curation: when the system becomes your personal docent},
	url = {http://ceur-ws.org/Vol-2225/paper6.pdf},
	abstract = {Curation is the act of selecting, organizing, and presenting content. Some
applications emulate this process by turning users into curators, while
others use recommenders to select items, seldom achieving the focus or
selectivity of human curators. We bridge this gap with a …},
	author = {Dragovic, Nevena and Azpiazu, Ion Madrazo and Pera, Maria Soledad},
	month = oct,
	year = {2018},
	pages = {37--44},
}



@unpublished{aridor_economics_2022,
	title = {The {Economics} of {Recommender} {Systems}: {Evidence} from a {Field} {Experiment} on {MovieLens}},
	url = {http://arxiv.org/abs/2211.14219},
	abstract = {We conduct a field experiment on a movie-recommendation platform to
identify if and how recommendations affect consumption. We use
within-consumer randomization at the good level and elicit beliefs about
unconsumed goods to disentangle exposure from informational effects. We
find recommendations increase consumption beyond its role in exposing
goods to consumers. We provide support for an informational mechanism:
recommendations affect consumers' beliefs, which in turn explain
consumption. Recommendations reduce uncertainty about goods consumers are
most uncertain about and induce information acquisition. Our results
highlight the importance of recommender systems' informational role when
considering policies targeting these systems in online marketplaces.},
	author = {Aridor, Guy and Goncalves, Duarte and Kluver, Daniel and Kong, Ruoyan and Konstan, Joseph},
	month = nov,
	year = {2022},
	note = {ISBN: 2211.14219
Publication Title: arXiv [econ.GN]},
}



@article{ibrahim_hybrid_2021,
	title = {Hybrid {Recommender} for {Research} {Papers} and {Articles}},
	volume = {10},
	url = {http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf},
	abstract = {… GroupLens called LensKit , along with set of tools for such system was
used to implement Collaborative filtering algorithm. This research uses
only the LensKit -core and LensKit -data-structures modules to implement
this section of the algorithm …},
	number = {2},
	journal = {Int. J. Intell. Inf. Database Syst.},
	author = {Ibrahim, Alhassan Jamilu and Zira, Peter and Abdulganiyyi, Nuraini},
	year = {2021},
	note = {Publisher: Science Publishing Group},
	pages = {9},
}



@inproceedings{wei_recommender_2021,
	address = {New York, NY, USA},
	title = {Recommender {Systems} for {Software} {Project} {Managers}},
	url = {https://doi.org/10.1145/3463274.3463951},
	doi = {10.1145/3463274.3463951},
	abstract = {The design of recommendation systems is based on complex information
processing and big data interaction. This personalized view has evolved
into a hot area in the past decade, where applications might have been
proved to help for solving problem in the software development field.
Therefore, with the evolvement of Recommendation System in Software
Engineering (RSSE), the coordination of software projects with their
stakeholders is improving. This experiment examines four open source
recommender systems and implemented a customized recommender engine with
two industrial-oriented packages: Lenskit and Mahout. Each of the main
functions was examined and issues were identified during the experiment.},
	urldate = {2021-09-14},
	booktitle = {{EASE} 2021},
	publisher = {Association for Computing Machinery},
	author = {Wei, Liang and Capretz, Luiz Fernando},
	month = jun,
	year = {2021},
	note = {Journal Abbreviation: EASE 2021},
	keywords = {RSSE, Recommender Engine, Project Management, Recommendation System, Recommendation System in Software Engineering},
	pages = {412--417},
}



@inproceedings{zhou_privacy_2021,
	title = {Privacy and performance in recommender systems: {Exploration} of potential influence of {CCPA}},
	url = {http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf},
	urldate = {2021-07-12},
	author = {Zhou, Meizi and Song, Yicheng and Adomavicius, Gediminas},
	year = {2021},
}



@article{wischenbart_engaging_2021,
	title = {Engaging end-user driven recommender systems: personalization through web augmentation},
	volume = {80},
	issn = {1380-7501},
	url = {https://doi.org/10.1007/s11042-020-09803-8},
	doi = {10.1007/s11042-020-09803-8},
	abstract = {In the past decades recommender systems have become a powerful tool to
improve personalization on the Web. Yet, many popular websites lack such
functionality, its implementation usually requires certain technical
skills, and, above all, its introduction is beyond the scope and control
of end-users. To alleviate these problems, this paper presents a novel
tool to empower end-users without programming skills, without any
involvement of website providers, to embed personalized recommendations of
items into arbitrary websites on client-side. For this we have developed a
generic meta-model to capture recommender system configuration parameters
in general as well as in a web augmentation context. Thereupon, we have
implemented a wizard in the form of an easy-to-use browser plug-in,
allowing the generation of so-called user scripts, which are executed in
the browser to engage collaborative filtering functionality from a
provided external rest service. We discuss functionality and limitations
of the approach, and in a study with end-users we assess the usability and
show its suitability for combining recommender systems with web
augmentation techniques, aiming to empower end-users to implement
controllable recommender applications for a more personalized browsing
experience.},
	number = {5},
	journal = {Multimed. Tools Appl.},
	author = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Bosetti, Gabriela and Kapsammer, Elisabeth},
	month = feb,
	year = {2021},
	pages = {6785--6809},
}



@unpublished{bellogin_improving_2021,
	title = {Improving {Accountability} in {Recommender} {Systems} {Research} {Through} {Reproducibility}},
	url = {http://arxiv.org/abs/2102.00482},
	abstract = {Reproducibility is a key requirement for scientific progress. It allows
the reproduction of the works of others, and, as a consequence, to fully
trust the reported claims and results. In this work, we argue that, by
facilitating reproducibility of recommender systems experimentation, we
indirectly address the issues of accountability and transparency in
recommender systems research from the perspectives of practitioners,
designers, and engineers aiming to assess the capabilities of published
research works. These issues have become increasingly prevalent in recent
literature. Reasons for this include societal movements around intelligent
systems and artificial intelligence striving towards fair and objective
use of human behavioral data (as in Machine Learning, Information
Retrieval, or Human-Computer Interaction). Society has grown to expect
explanations and transparency standards regarding the underlying
algorithms making automated decisions for and around us. This work surveys
existing definitions of these concepts, and proposes a coherent
terminology for recommender systems research, with the goal to connect
reproducibility to accountability. We achieve this by introducing several
guidelines and steps that lead to reproducible and, hence, accountable
experimental workflows and research. We additionally analyze several
instantiations of recommender system implementations available in the
literature, and discuss the extent to which they fit in the introduced
framework. With this work, we aim to shed light on this important problem,
and facilitate progress in the field by increasing the accountability of
research.},
	author = {Bellogín, Alejandro and Said, Alan},
	month = jan,
	year = {2021},
	note = {ISBN: 2102.00482
Publication Title: arXiv [cs.IR]},
}



@article{cheng_understanding_2020,
	title = {Understanding the {Impact} of {Individual} {Users}’ {Rating} {Characteristics} on the {Predictive} {Accuracy} of {Recommender} {Systems}},
	volume = {32},
	issn = {1091-9856},
	url = {https://doi.org/10.1287/ijoc.2018.0882},
	doi = {10.1287/ijoc.2018.0882},
	abstract = {In this study, we investigate how individual users? rating characteristics
affect the user-level performance of recommendation algorithms. We measure
users? rating characteristics from three perspectives: rating value,
rating structure, and neighborhood network embeddedness. We study how
these three categories of measures influence the predictive accuracy of
popular recommendation algorithms for each user. Our experiments use five
real-world data sets with varying characteristics. For each individual
user, we estimate the predictive accuracy of three recommendation
algorithms. We then apply regression-based models to uncover the
relationships between rating characteristics and recommendation
performance at the individual user level. Our experimental results show
consistent and significant effects of several rating measures on
recommendation accuracy. Understanding how rating characteristics affect
the recommendation performance at the individual user level has practical
implications for the design of recommender systems.},
	number = {2},
	journal = {INFORMS J. Comput.},
	author = {Cheng, Xiaoye and Zhang, Jingjing and Yan, Lu (lucy)},
	month = apr,
	year = {2020},
	note = {Publisher: INFORMS},
	pages = {303--320},
}



@inproceedings{coba_rrecsys_2016,
	title = {rrecsys: {An} {R}-package for {Prototyping} {Recommendation} {Algorithms}},
	url = {https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf},
	abstract = {ABSTRACT We introduce rrecsys , an open source extension package in R for
rapid prototyping and intuitive assessment of recommender system
algorithms. As the only currently available R package for recommender
algorithms (recommenderlab) did not},
	author = {Çoba, Ludovik and Zanker, Markus},
	year = {2016},
	keywords = {toolkit},
}



@phdthesis{saha_multi-objective_2016,
	title = {A {Multi}-objective {Autotuning} {Framework} {For} {The} {Java} {Virtual} {Machine}},
	url = {https://digital.library.txstate.edu/handle/10877/6096},
	abstract = {Due to inherent limitations in performance, Java was not considered a
suitable platform for for scalable high-performance computing (HPC) for a
long time. The scenario is changing because of the development of
frameworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in
usage, achieving high performance with Java is not trivial. High
performance in Java relies on libraries providing explicit threads or
relying on runnable-like interfaces for distributed programming. In this
thesis, we develop an autotuning framework for JVM that manages multiple
objective functions including execution time, power consumption, energy
and perfomance-per-watt. The framework searches the combined space of JIT
optimization sequences and different classes of JVM runtime parameters. To
discover good configurations more quickly, the framework implements novel
heuristic search algorithms. To reduce the size of the search space
machine-learning based pruning techniques are used. Evaluation on
recommender system workloads show that significant improvements in both
performance and power can be gained by fine-tuning JVM runitme parameters.},
	urldate = {2016-07-05},
	school = {Texas State University},
	author = {Saha, Shuvabrata},
	month = apr,
	year = {2016},
}



@article{asenova_personalized_2019,
	title = {Personalized {Micro}-{Service} {Recommendation} {System} for {Online} {News}},
	volume = {160},
	issn = {1877-0509},
	url = {http://www.sciencedirect.com/science/article/pii/S1877050919317399},
	doi = {10.1016/j.procs.2019.11.039},
	abstract = {In the era of artificial intelligence and high technology advance our life
is dependent on them in every aspect. The dynamic environment forces us to
plan our time with conscious and every minute is valuable. To help
individuals and corporations see information that is only relevant to
them, recommendation systems are in place. Popular platforms that such as
Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation
systems to better serve the needed of their users. This research paper
gives insight of building a microservice recommendation system for online
news. Research in recommendation systems is mainly focused on improving
user’s experience based mainly on personalization information, such as
preferences, and searching history. To determine the initial preferences
of a user an initial menu of topics/themes is provided for the user to
choose from. In order to reflect as precise as possible the searching
interests regarding news of user, all of his interactions are thoroughly
recorded and in depth analyzed, based on advanced machine learning
techniques, when adjusting the news topics, the user is interested for.
Based on the aforementioned approach, a personalized recommendation system
for online news has been developed. Existing techniques has been
researched and evaluated to aid the decision about picking the best
approach for the software to be implemented. Frameworks/technologies used
for the development are Java 8, Spring boot, Spring MVC, Maven and
MongoDB.},
	journal = {Procedia Comput. Sci.},
	author = {Asenova, Marchela and Chrysoulas, Christos},
	month = jan,
	year = {2019},
	keywords = {TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases},
	pages = {610--615},
}



@article{kotkov_how_2020,
	title = {How does serendipity affect diversity in recommender systems? {A} serendipity-oriented greedy algorithm},
	volume = {102},
	issn = {0144-3097},
	url = {http://link.springer.com/10.1007/s00607-018-0687-5},
	doi = {10.1007/s00607-018-0687-5},
	abstract = {Most recommender systems suggest items that are popular among all users
and similar to items a user usually consumes. As a result, the user
receives recommendations that she/he is already familiar with or would
find anyway, leading to low satisfaction. To overcome this problem, a
recommender system should suggest novel, relevant and unexpected i.e.,
serendipitous items. In this paper, we propose a serendipity-oriented,
reranking algorithm called a serendipity-oriented greedy (SOG) algorithm,
which improves serendipity of recommendations through feature
diversification and helps overcome the overspecialization problem. To
evaluate our algorithm, we employed the only publicly available dataset
containing user feedback regarding serendipity. We compared our SOG
algorithm with topic diversification, popularity baseline, singular value
decomposition, serendipitous personalized ranking and Zheng’s algorithms
relying on the above dataset. SOG outperforms other algorithms in terms of
serendipity and diversity. It also outperforms serendipity-oriented
algorithms in terms of accuracy, but underperforms accuracy-oriented
algorithms in terms of accuracy. We found that the increase of diversity
can hurt accuracy and harm or improve serendipity depending on the size of
diversity increase.},
	number = {2},
	journal = {Computing},
	author = {Kotkov, Denis and Veijalainen, Jari and Wang, Shuaiqiang},
	month = feb,
	year = {2020},
	pages = {393--411},
}



@inproceedings{shriver_evaluating_2019,
	title = {Evaluating {Recommender} {System} {Stability} with {Influence}-{Guided} {Fuzzing}},
	url = {https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf},
	abstract = {Recommender systems help users to find products or services they may like
when lacking personal experience or facing an overwhelming set of choices.
Since unstable recommendations can lead to distrust, loss of profits, and
a poor user experience, it is important to test recommender system
stability. In this work, we present an approach based on inferred models
of influence that underlie recommender systems to guide the generation of
dataset modifications to assess a recommender's stability. We implement
our approach …},
	publisher = {AAAI},
	author = {Shriver, David and Elbaum, Sebastian and Dwyer, Matthew B and Rosenblum, David S},
	year = {2019},
}



@article{cami_user_2018,
	title = {User preferences modeling using dirichlet process mixture model for a content-based recommender system},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705118304799},
	doi = {10.1016/j.knosys.2018.09.028},
	abstract = {Recommender systems have been developed to assist users in retrieving
relevant resources. Collaborative and content-based filtering are two
basic approaches that are used in recommender systems. The former employs
the feedback of users with similar interests, while the latter is based on
the feature of the selected resources by each user. Recommender systems
can consider users’ behavior to more accurately estimate their preferences
via a list of recommendations. However, the existing approaches rarely
consider both interests and preferences of the users. Also, the dynamic
nature of user behavior poses an additional challenge for recommender
systems. In this paper, we consider the interactions of each individual
user, and analyze them to propose a user model and capture user’s
interests. We construct the user model based on a Bayesian nonparametric
framework, called the Dirichlet Process Mixture Model. The proposed model
evolves following the dynamic nature of user behavior to adapt both the
user interests and preferences. We implemented the proposed model and
evaluated it using both the MovieLens dataset, and a real-world dataset
that contains news tweets from five news channels (New York Times, BBC,
CNN, Reuters and Associated Press). The experimental results and
comparisons with several recently developed approaches show the
superiority in accuracy of the proposed approach, and its ability to adapt
with user behavior over time.},
	journal = {Knowledge-Based Systems},
	author = {Cami, Bagher Rahimpour and Hassanpour, Hamid and Mashayekhi, Hoda},
	month = sep,
	year = {2018},
	keywords = {Temporal content-based recommender systems, User behavior modeling, User preferences modeling},
}



@inproceedings{carvalho_fair_2018,
	title = {{FAiR}: {A} {Framework} for {Analyses} and {Evaluations} on {Recommender} {Systems}},
	url = {http://dx.doi.org/10.1007/978-3-319-95168-3_26},
	doi = {10.1007/978-3-319-95168-3_26},
	abstract = {Recommender systems (RSs) have become essential tools in e-commerce
applications, helping users in the decision-making process. Evaluation on
these tools is, however, a major divergence point nowadays, since there is
no consensus regarding which metrics are necessary to consolidate new RSs.
For this reason, distinct frameworks have been developed to ease the
deployment of RSs in research and/or production environments. In the
present work, we perform an extensive study of the most popular evaluation
metrics, organizing them into three groups: Effectiveness-based,
Complementary Dimensions of Quality and Domain Profiling. Further, we
consolidate a framework named FAiR to help researchers in evaluating their
RSs using these metrics, besides identifying the characteristics of data
collections that may intrinsically affect RSs performance. FAiR is
compatible with the output format of the main existing RSs libraries
(i.e., MyMediaLite and LensKit).},
	publisher = {Springer International Publishing},
	author = {Carvalho, Diego and Silva, Nícollas and Silveira, Thiago and Mourão, Fernando and Pereira, Adriano and Dias, Diego and Rocha, Leonardo},
	year = {2018},
	pages = {383--397},
}



@inproceedings{coba_replicating_2018,
	address = {New York, NY, USA},
	title = {Replicating and {Improving} {Top}-{N} {Recommendations} in {Open} {Source} {Packages}},
	url = {http://doi.acm.org/10.1145/3227609.3227671},
	doi = {10.1145/3227609.3227671},
	booktitle = {{WIMS} '18},
	publisher = {ACM},
	author = {Coba, Ludovik and Symeonidis, Panagiotis and Zanker, Markus},
	year = {2018},
	note = {Journal Abbreviation: WIMS '18},
	keywords = {Collaborative Filtering, Recommendation algorithms, evaluation},
	pages = {40:1--40:7},
}



@article{yang_improving_2018,
	title = {Improving {Existing} {Collaborative} {Filtering} {Recommendations} via {Serendipity}-{Based} {Algorithm}},
	volume = {20},
	issn = {1520-9210},
	url = {http://dx.doi.org/10.1109/TMM.2017.2779043},
	doi = {10.1109/TMM.2017.2779043},
	abstract = {In this paper, we study how to address the sparsity, accuracy and
serendipity issues of top-N recommendation with collaborative filtering
(CF). Existing studies commonly use rated items (which form only a small
section in a rating matrix) or import some additional information (e.g.,
details about the items and users) to improve the performance of CF.
Unlike these methods, we propose a novel notion towards a huge amount of
unrated items: serendipity item. By utilizing serendipity items, we
propose concise satisfaction and interest injection (CSII), a method that
can effectively find interesting, satisfying, and serendipitous items in
unrated items. By preventing uninteresting and unsatisfying items to be
recommended as top-N items, this concise-but-novel method improves
accuracy and recommendation quality (especially serendipity)
substantially. Meanwhile, it can address the sparsity and cold-start
issues by enriching the rating matrix in CF without additional
information. As our method tackles rating matrix before recommendation
procedure, it can be applied to most existing CF methods, such as
item-based CF, user-based CF and matrix factorization-based CF. Through
comprehensive experiments using abundant real-world datasets with LensKit
implementation, we successfully demonstrate that our solution improves the
performance of existing CF methods consistently and universally. Moreover,
comparing with baseline methods, CSII can extract uninteresting items more
carefully and cautiously, avoiding potential items inferred by mistake.},
	number = {7},
	journal = {IEEE Trans. Multimedia},
	author = {Yang, Y and Xu, Y and Wang, E and Han, J and Yu, Z},
	month = jul,
	year = {2018},
	keywords = {CF methods, CSII, Collaboration, Collaborative filtering, Computer science, Data mining, Lifting equipment, Multimedia communication, Recommender systems, cold-start issues, collaborative filtering, collaborative filtering recommendations, concise satisfaction and interest injection, item-based CF, matrix decomposition, matrix factorization, matrix factorization-based CF, rating matrix, recommendation quality, recommender systems, serendipitous recommendation, serendipity item, top-N items, top-N recommendation, unrated items, user-based CF},
	pages = {1888--1900},
}



@mastersthesis{shriver_assessing_2018,
	title = {Assessing the {Quality} and {Stability} of {Recommender} {Systems}},
	url = {https://digitalcommons.unl.edu/computerscidiss/147},
	abstract = {Recommender systems help users to find products they may like when lacking
personal experience or facing an overwhelmingly large set of items.
However, assessing the quality and stability of recommender systems can
present challenges for developers. First, traditional accuracy metrics,
such as precision and recall, for validating the quality of
recommendations, offer only a coarse, one-dimensional view of the system
performance. Second, assessing the stability of a recommender systems
requires generating new data and retraining a system, which is expensive.
In this work, we present two new approaches for assessing the quality and
stability of recommender systems to address these challenges. We first
present a general and extensible approach for assessing the quality of the
behavior of a recommender system using logical property templates. The
approach is general in that it defines recommendation systems in terms of
sets of rankings, ratings, users, and items on which property templates
are defined. It is extensible in that these property templates define a
space of properties that can be instantiated and parameterized to
characterize a recommendation system. We study the application of the
approach to several recommendation systems. Our findings demonstrate the
potential of these properties, illustrating the insights they can provide
about the different algorithms and evolving datasets. We also present an
approach for influence-guided fuzz testing of recommender system
stability. We infer influence models for aspects of a dataset, such as
users or items, from the recommendations produced by a recommender system
and its training data. We define dataset fuzzing heuristics that use these
influence models for generating modifications to an original dataset and
we present a test oracle based on a threshold of acceptable instability.
We implement our approach and evaluate it on several recommender
algorithms using the MovieLens dataset and we find that influence-guided
fuzzing can effectively find small sets of modifications that cause
significantly more instability than random approaches. Adviser: Sebastian
Elbaum},
	urldate = {2018-05-08},
	school = {University of Nebraska - Lincoln},
	author = {Shriver, David},
	collaborator = {Elbaum, Sebastian},
	year = {2018},
	note = {Publication Title: Computer Science and Engineering},
}



@inproceedings{colucci_evaluating_2016,
	address = {New York, NY, USA},
	title = {Evaluating {Item}-{Item} {Similarity} {Algorithms} for {Movies}},
	url = {http://doi.acm.org/10.1145/2851581.2892362},
	doi = {10.1145/2851581.2892362},
	booktitle = {{CHI} {EA} '16},
	publisher = {ACM},
	author = {Colucci, Lucas and Doshi, Prachi and Lee, Kun-Lin and Liang, Jiajie and Lin, Yin and Vashishtha, Ishan and Zhang, Jia and Jude, Alvin},
	year = {2016},
	note = {Journal Abbreviation: CHI EA '16},
	keywords = {algorithm evaluation, item-item similarity, recommender systems},
	pages = {2141--2147},
}



@inproceedings{said_comparative_2014,
	address = {New York, NY, USA},
	title = {Comparative {Recommender} {System} {Evaluation}: {Benchmarking} {Recommendation} {Frameworks}},
	url = {http://dx.doi.org/10.1145/2645710.2645746},
	doi = {10.1145/2645710.2645746},
	abstract = {Recommender systems research is often based on comparisons of predictive
accuracy: the better the evaluation scores, the better the recommender.
However, it is difficult to compare results from different recommender
systems due to the many options in design and implementation of an
evaluation strategy. Additionally, algorithmic implementations can diverge
from the standard formulation due to manual tuning and modifications that
work better in some situations. In this work we compare common
recommendation algorithms as implemented in three popular recommendation
frameworks. To provide a fair comparison, we have complete control of the
evaluation dimensions being benchmarked: dataset, data splitting,
evaluation strategies, and metrics. We also include results using the
internal evaluation mechanisms of these frameworks. Our analysis points to
large differences in recommendation accuracy across frameworks and
strategies, i.e. the same baselines may perform orders of magnitude better
or worse across frameworks. Our results show the necessity of clear
guidelines when reporting evaluation of recommender systems to ensure
reproducibility and comparison of results.},
	urldate = {2017-02-03},
	booktitle = {{RecSys} '14},
	publisher = {ACM Press},
	author = {Said, Alan and Bellogin, Alejandro},
	month = oct,
	year = {2014},
	note = {Journal Abbreviation: RecSys '14},
	keywords = {toolkit},
	pages = {129--136},
}



@inproceedings{harper_putting_2015,
	address = {New York, NY, USA},
	title = {Putting {Users} in {Control} of {Their} {Recommendations}},
	url = {http://doi.acm.org/10.1145/2792838.2800179},
	doi = {10.1145/2792838.2800179},
	abstract = {The essence of a recommender system is that it can recommend items
personalized to the preferences of an individual user. But typically users
are given no explicit control over this personalization, and are instead
left guessing about how their actions affect the resulting
recommendations. We hypothesize that any recommender algorithm will better
fit some users' expectations than others, leaving opportunities for
improvement. To address this challenge, we study a recommender that puts
some control in the hands of users. Specifically, we build and evaluate a
system that incorporates user-tuned popularity and recency modifiers,
allowing users to express concepts like "show more popular items". We find
that users who are given these controls evaluate the resulting
recommendations much more positively. Further, we find that users diverge
in their preferred settings, confirming the importance of giving control
to users.},
	urldate = {2015-09-19},
	booktitle = {{RecSys} '15},
	publisher = {ACM},
	author = {Harper, F Maxwell and Xu, Funing and Kaur, Harmanpreet and Condiff, Kyle and Chang, Shuo and Terveen, Loren},
	year = {2015},
	note = {Journal Abbreviation: RecSys '15},
	pages = {3--10},
}



@phdthesis{ekstrand_towards_2014,
	address = {Minneapolis, MN},
	title = {Towards {Recommender} {Engineering}: {Tools} and {Experiments} in {Recommender} {Differences}},
	url = {http://hdl.handle.net/11299/165307},
	abstract = {Since the introduction of their modern form 20 years ago, recommender
systems have proven a valuable tool for help users manage information
overload. Two decades of research have produced many algorithms for
computing recommendations, mechanisms for evaluating their effectiveness,
and user interfaces and experiences to embody them. It has also been found
that the outputs of different recommendation algorithms differ in
user-perceptible ways that affect their suitability to different tasks and
information needs. However, there has been little work to systematically
map out the space of algorithms and the characteristics they exhibit that
makes them more or less effective in different applications. As a result,
developers of recommender systems must experiment, conducting basic
science on each application and its users to determine the approach(es)
that will meet their needs. This thesis presents our work towards
recommender engineering: the design of recommender systems from
well-understood principles of user needs, domain properties, and algorithm
behaviors. This will reduce the experimentation required for each new
recommender application, allowing developers to design recommender systems
that are likely to be effective for their particular application. To that
end, we make four contributions: the LensKit toolkit for conducting
experiments on a wide variety of recommender algorithms and data sets
under different experimental conditions (offline experiments with diverse
metrics, online user studies, and the ability to grow to support
additional methodologies), along with new developments in object-oriented
software configuration to support this toolkit; experiments on the
configuration options of widely-used algorithms to provide guidance on
tuning and configuring them; an offline experiment on the differences in
the errors made by different algorithms; and a user study on the
user-perceptible differences between lists of movie recommendations
produced by three common recommender algorithms. Much research is needed
to fully realize the vision of recommender engineering in the coming
years; it is our hope that LensKit will prove a valuable foundation for
much of this work, and our experiments represent a small piece of the
kinds of studies that must be carried out, replicated, and validated to
enable recommender systems to be engineered.},
	school = {University of Minnesota},
	author = {Ekstrand, Michael D},
	collaborator = {Konstan, Joseph A},
	month = jul,
	year = {2014},
	note = {Publication Title: Computer Science and Engineering
Volume: Ph.D},
}



@inproceedings{ekstrand_when_2012,
	address = {New York, NY, USA},
	title = {When recommenders fail: predicting recommender failure for algorithm selection and combination},
	url = {http://doi.acm.org/10.1145/2365952.2366002},
	doi = {10.1145/2365952.2366002},
	abstract = {Hybrid recommender systems --- systems using multiple algorithms together
to improve recommendation quality --- have been well-known for many years
and have shown good performance in recent demonstrations such as the
NetFlix Prize. Modern hybridization techniques, such as feature-weighted
linear stacking, take advantage of the hypothesis that the relative
performance of recommenders varies by circumstance and attempt to optimize
each item score to maximize the strengths of the component recommenders.
Less attention, however, has been paid to understanding what these
strengths and failure modes are. Understanding what causes particular
recommenders to fail will facilitate better selection of the component
recommenders for future hybrid systems and a better understanding of how
individual recommender personalities can be harnessed to improve the
recommender user experience. We present an analysis of the predictions
made by several well-known recommender algorithms on the MovieLens 10M
data set, showing that for many cases in which one algorithm fails, there
is another that will correctly predict the rating.},
	urldate = {2012-12-13},
	booktitle = {{RecSys} '12},
	publisher = {ACM},
	author = {Ekstrand, Michael D and Riedl, John T},
	year = {2012},
	note = {Journal Abbreviation: RecSys '12},
	pages = {233--236},
}



@inproceedings{kluver_how_2012,
	address = {New York, NY, USA},
	title = {How many bits per rating?},
	url = {http://doi.acm.org/10.1145/2365952.2365974},
	doi = {10.1145/2365952.2365974},
	abstract = {Most recommender systems assume user ratings accurately represent user
preferences. However, prior research shows that user ratings are imperfect
and noisy. Moreover, this noise limits the measurable predictive power of
any recommender system. We propose an information theoretic framework for
quantifying the preference information contained in ratings and
predictions. We computationally explore the properties of our model and
apply our framework to estimate the efficiency of different rating scales
for real world datasets. We then estimate how the amount of information
predictions give to users is related to the scale ratings are collected
on. Our findings suggest a tradeoff in rating scale granularity: while
previous research indicates that coarse scales (such as thumbs up / thumbs
down) take less time, we find that ratings with these scales provide less
predictive value to users. We introduce a new measure, preference bits per
second, to quantitatively reconcile this tradeoff.},
	urldate = {2013-09-12},
	booktitle = {{RecSys} '12},
	publisher = {ACM},
	author = {Kluver, Daniel and Nguyen, Tien T and Ekstrand, Michael and Sen, Shilad and Riedl, John},
	year = {2012},
	note = {Journal Abbreviation: RecSys '12},
	pages = {99--106},
}



@inproceedings{nguyen_rating_2013,
	address = {New York, NY, USA},
	title = {Rating {Support} {Interfaces} to {Improve} {User} {Experience} and {Recommender} {Accuracy}},
	url = {http://doi.acm.org/10.1145/2507157.2507188},
	doi = {10.1145/2507157.2507188},
	abstract = {One of the challenges for recommender systems is that users struggle to
accurately map their internal preferences to external measures of quality
such as ratings. We study two methods for supporting the mapping process:
(i) reminding the user of characteristics of items by providing
personalized tags and (ii) relating rating decisions to prior rating
decisions using exemplars. In our study, we introduce interfaces that
provide these methods of support. We also present a set of methodologies
to evaluate the efficacy of the new interfaces via a user experiment. Our
results suggest that presenting exemplars during the rating process helps
users rate more consistently, and increases the quality of the data.},
	urldate = {2014-04-28},
	booktitle = {{RecSys} '13},
	publisher = {ACM},
	author = {Nguyen, Tien T and Kluver, Daniel and Wang, Ting-Yu and Hui, Pik-Mai and Ekstrand, Michael D and Willemsen, Martijn C and Riedl, John},
	year = {2013},
	note = {Journal Abbreviation: RecSys '13},
	pages = {149--156},
}



@inproceedings{konstan_teaching_2014,
	address = {New York, NY, USA},
	title = {Teaching {Recommender} {Systems} at {Large} {Scale}: {Evaluation} and {Lessons} {Learned} from a {Hybrid} {MOOC}},
	url = {http://doi.acm.org/10.1145/2556325.2566244},
	doi = {10.1145/2556325.2566244},
	abstract = {In Fall 2013 we offered an open online Introduction to Recommender Systems
through Coursera, while simultaneously offering a for-credit version of
the course on-campus using the Coursera platform and a flipped classroom
instruction model. As the goal of offering this course was to experiment
with this type of instruction, we performed extensive evaluation including
surveys of demographics, self-assessed skills, and learning intent; we
also designed a knowledge-assessment tool specifically for the subject
matter in this course, administering it before and after the course to
measure learning. We also tracked students through the course, including
separating out students enrolled for credit from those enrolled only for
the free, open course. This article reports on our findings.},
	urldate = {2014-03-19},
	booktitle = {L@{S} '14},
	publisher = {ACM},
	author = {Konstan, Joseph A and Walker, J D and Brooks, D Christopher and Brown, Keith and Ekstrand, Michael D},
	month = mar,
	year = {2014},
	note = {Journal Abbreviation: L@S '14},
	pages = {61--70},
}



@inproceedings{sardianos_scaling_2017,
	title = {Scaling {Collaborative} {Filtering} to {Large}-{Scale} {Bipartite} {Rating} {Graphs} {Using} {Lenskit} and {Spark}},
	url = {http://dx.doi.org/10.1109/BigDataService.2017.28},
	doi = {10.1109/BigDataService.2017.28},
	abstract = {Popular social networking applications such as Facebook, Twitter,
Friendster, etc. generate very large graphs with different
characteristics. These social networks are huge, comprising millions of
nodes and edges that push existing graph mining algorithms and
architectures to their limits. In product-rating graphs, users connect
with each other and rate items in tandem. In such bipartite graphs users
and items are the nodes and ratings are the edges and collaborative
filtering algorithms use the edge information (i.e. user ratings for
items) in order to suggest items of potential interest to users. Existing
algorithms can hardly scale up to the size of the entire graph and require
unlimited resources to finish. This work employs a machine learning method
for predicting the performance of Collaborative Filtering algorithms using
the structural features of the bipartite graphs. Using a fast graph
partitioning algorithm and information from the user friendship graph, the
original bipartite graph is partitioned into different schemes (i.e. sets
of smaller bipartite graphs). The schemes are evaluated against the
predicted performance of the Collaborative Filtering algorithm and the
best partitioning scheme is employed for generating the recommendations.
As a result, the Collaborative Filtering algorithms are applied to smaller
bipartite graphs, using limited resources and allowing the problem to
scale or be parallelized. Tests on a large, real-life, rating graph, show
that the proposed method allows the collaborative filtering algorithms to
run in parallel and complete using limited resources.},
	author = {Sardianos, C and Varlamis, I and Eirinaki, M},
	month = apr,
	year = {2017},
	keywords = {Bipartite graph, Collaboration, Collaborative Filtering, Graph Metrics, Graph Partitioning, Lenskit, Machine learning algorithms, Partitioning algorithms, Prediction algorithms, Recommender Systems, Recommender systems, Social Networks, Social network services, Spark, bipartite graphs, collaborative filtering, collaborative filtering algorithms, data mining, fast graph partitioning algorithm, graph theory, large-scale bipartite rating graphs, learning (artificial intelligence), machine learning, product-rating graphs, social networking (online), social networking applications, structural features, user-friendship graph},
	pages = {70--79},
}



@book{kotkov_serendipity_2018,
	title = {Serendipity in recommender systems},
	isbn = {978-951-39-7438-1},
	url = {https://jyx.jyu.fi/handle/123456789/58207},
	abstract = {The number of goods and services (such as accommodation or music
streaming) offered by e-commerce websites does not allow users to examine
all the available options in a reasonable amount of time. Recommender
systems are auxiliary systems designed to help users ﬁnd interesting goods
or services (items) on a website when the number of available items is
overwhelming. Traditionally, recommender systems have been optimized for
accuracy, which indicates how often a user consumed the items recommended
by system. To increase accuracy, recommender systems often suggest items
that are popular and suitably similar to items these users have consumed
in the past. As a result, users often lose interest in using these
systems, as they either know about the recommended items already or can
easily ﬁnd these items themselves. One way to increase user satisfaction
and user retention is to suggest serendipitous items. These items are
items that users would not ﬁnd themselves or even look for, but would
enjoy consuming. Serendipity in recommender systems has not been
thoroughly investigated. There is not even a consensus on the concept’s
deﬁnition. In this dissertation, serendipitous items are deﬁned as
relevant, novel and unexpected to a user. In this dissertation, we (a)
review different deﬁnitions of the concept and evaluate them in a user
study, (b) assess the proportion of serendipitous items in a typical
recommender system, (c) review ways to measure and improve serendipity,
(d) investigate serendipity in cross-domain recommender systems (systems
that take advantage of multiple domains, such as movies, songs and books)
and (e) discuss challenges and future directions concerning this topic. We
applied a Design Science methodology as the framework for this study and
developed four artifacts: (1) a collection of eight variations of
serendipity deﬁnition, (2) a measure of the serendipity of suggested
items, (3) an algorithm that generates serendipitous suggestions, (4) a
dataset of user feedback regarding serendipitous movies in the recommender
system MovieLens. These artifacts are evaluated using suitable methods and
communicated through publications.},
	urldate = {2018-07-06},
	publisher = {University of Jyväskylä},
	author = {Kotkov, Denis},
	year = {2018},
}



@phdthesis{noffsinger_predictive_2020,
	address = {Ann Arbor, United States},
	title = {Predictive {Accuracy} of {Recommender} {Algorithms}},
	url = {https://libproxy.boisestate.edu/login?url=https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2},
	abstract = {Recommender systems present a customized list of items based upon user or
item characteristics with the objective of reducing a large number of
possible choices to a smaller ranked set most likely to appeal to the
user. A variety of algorithms for recommender systems have been developed
and refined including applications of deep learning neural networks.
Recent research reports point to a need to perform carefully controlled
experiments to gain insights about the relative accuracy of different
recommender algorithms, because studies evaluating different methods have
not used a common set of benchmark data sets, baseline models, and
evaluation metrics.The dissertation used publicly available sources of
ratings data with a suite of three conventional recommender algorithms and
two deep learning (DL) algorithms in controlled experiments to assess
their comparative accuracy. Results for the non-DL algorithms conformed
well to published results and benchmarks. The two DL algorithms did not
perform as well and illuminated known challenges implementing DL
recommender algorithms as reported in the literature. Model overfitting is
discussed as a potential explanation for the weaker performance of the DL
algorithms and several regularization strategies are reviewed as possible
approaches to improve predictive error. Findings justify the need for
further research in the use of deep learning models for recommender
systems.},
	school = {Nova Southeastern University},
	author = {Noffsinger, William B},
	collaborator = {Mukherjee, Sumitra},
	year = {2020},
	note = {Publication Title: Information Systems (DISS)},
}



@article{gazdar_new_2020,
	title = {A new similarity measure for collaborative filtering based recommender systems},
	volume = {188},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705119304484},
	doi = {10.1016/j.knosys.2019.105058},
	abstract = {The objective of a recommender system is to provide customers with
personalized recommendations while selecting an item among a set of
products (movies, books, etc.). The collaborative filtering is the most
used technique for recommender systems. One of the main components of a
recommender system based on the collaborative filtering technique, is the
similarity measure used to determine the set of users having the same
behavior with regard to the selected items. Several similarity functions
have been proposed, with different performances in terms of accuracy and
quality of recommendations. In this paper, we propose a new simple and
efficient similarity measure. Its mathematical expression is determined
through the following paper contributions: 1) transforming some intuitive
and qualitative conditions, that should be satisfied by the similarity
measure, into relevant mathematical equations namely: the integral
equation, the linear system of differential equations and a non-linear
system and 2) resolving the equations to achieve the kernel function of
the similarity measure. The extensive experimental study driven on a
benchmark datasets shows that the proposed similarity measure is very
competitive, especially in terms of accuracy, with regards to some
representative similarity measures of the literature.},
	journal = {Knowledge-Based Systems},
	author = {Gazdar, Achraf and Hidri, Lotfi},
	month = jan,
	year = {2020},
	keywords = {Collaborative filtering, Neighborhood based CF, Recommendation systems, Similarity measure},
	pages = {105058},
}



@inproceedings{polychronou_machine_2020,
	title = {Machine {Learning} {Algorithms} for {Food} {Intelligence}: {Towards} a {Method} for {More} {Accurate} {Predictions}},
	url = {http://dx.doi.org/10.1007/978-3-030-39815-6_16},
	doi = {10.1007/978-3-030-39815-6_16},
	abstract = {It is evident that machine learning algorithms are being widely impacting
industrial applications and platforms. Beyond typical research
experimentation scenarios, there is a need for companies that wish to
enhance their online data and analytics solutions to incorporate ways in
which they can select, experiment, benchmark, parameterise and choose the
version of a machine learning algorithm that seems to be most appropriate
for their specific application context. In this paper, we describe such a
need for a big data platform that supports food data analytics and
intelligence. More specifically, we introduce Agroknow’s big data platform
and identify the need to extend it with a flexible and interactive
experimentation environment where different machine learning algorithms
can be tested using a variation of synthetic and real data. A typical
usage scenario is described, based on our need to experiment with various
machine learning algorithms to support price prediction for food products
and ingredients. The initial requirements for an experimentation
environment are also introduced.},
	publisher = {Springer International Publishing},
	author = {Polychronou, Ioanna and Katsivelis, Panagis and Papakonstantinou, Mihalis and Stoitsis, Giannis and Manouselis, Nikos},
	year = {2020},
	pages = {165--172},
}



@inproceedings{karpus_things_2019,
	title = {Things you might not know about the k-{Nearest} neighbors algorithm},
	url = {https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf},
	author = {Karpus, Aleksandra and Raczyńska, M and Przybyłek, A},
	year = {2019},
}



@article{papadakis_scor_2017,
	title = {{SCoR}: {A} {Synthetic} {Coordinate} based {Recommender} system},
	volume = {79},
	issn = {0957-4174},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417417301070},
	doi = {10.1016/j.eswa.2017.02.025},
	abstract = {Recommender systems try to predict the preferences of users for specific
items, based on an analysis of previous consumer preferences. In this
paper, we propose SCoR, a Synthetic Coordinate based Recommendation system
which is shown to outperform the most popular algorithmic techniques in
the field, approaches like matrix factorization and collaborative
filtering. SCoR assigns synthetic coordinates to nodes (users and items),
so that the distance between a user and an item provides an accurate
prediction of the user’s preference for that item. The proposed framework
has several benefits. It is parameter free, thus requiring no fine tuning
to achieve high performance, and is more resistance to the cold-start
problem compared to other algorithms. Furthermore, it provides important
annotations of the dataset, such as the physical detection of users and
items with common and unique characteristics as well as the identification
of outliers. SCoR is compared against nine other state-of-the-art
recommender systems, sever of them based on the well known matrix
factorization and two on collaborative filtering. The comparison is
performed against four real datasets, including a brief version of the
dataset used in the well known Netflix challenge. The extensive
experiments prove that SCoR outperforms previous techniques while
demonstrating its improved stability and high performance.},
	journal = {Expert Syst. Appl.},
	author = {Papadakis, Harris and Panagiotakis, Costas and Fragopoulou, Paraskevi},
	month = aug,
	year = {2017},
	keywords = {Graph, Matrix factorization, Netflix, Recommender systems, Synthetic coordinates, Vivaldi},
	pages = {8--19},
}



@mastersthesis{solvang_video_2017,
	title = {Video {Recommendation} {Systems}: {Finding} a {Suitable} {Recommendation} {Approach} for an {Application} {Without} {Sufficient} {Data}},
	url = {http://hdl.handle.net/10852/59239},
	author = {Solvang, Marius Lørstad},
	year = {2017},
}



@article{pera_recommending_2017,
	title = {Recommending books to be exchanged online in the absence of wish lists},
	issn = {2330-1643},
	url = {http://dx.doi.org/10.1002/asi.23978},
	doi = {10.1002/asi.23978},
	abstract = {An online exchange system is a web service that allows communities to
trade items without the burden of manually selecting them, which saves
users' time and effort. Even though online book-exchange systems have been
developed, their services can further be improved by reducing the workload
imposed on their users. To accomplish this task, we propose a
recommendation-based book exchange system, called EasyEx, which identifies
potential exchanges for a user solely based on a list of items the user is
willing to part with. EasyEx is a novel and unique book-exchange system
because unlike existing online exchange systems, it does not require a
user to create and maintain a wish list, which is a list of items the user
would like to receive as part of the exchange. Instead, EasyEx directly
suggests items to users to increase serendipity and as a result expose
them to items which may be unfamiliar, but appealing, to them. In
identifying books to be exchanged, EasyEx employs known recommendation
strategies, that is, personalized mean and matrix factorization, to
predict book ratings, which are treated as the degrees of appeal to a user
on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which
solves constraint satisfaction problems efficiently, as part of the
recommendation-based exchange process to create exchange cycles.
Experimental results have verified that EasyEx offers users recommended
books that satisfy the users' interests and contributes to the
item-exchange mechanism with a new design methodology.},
	journal = {Journal of the Association for Information Science and Technology},
	author = {Pera, Maria Soledad and Ng, Yiu-Kai},
	month = nov,
	year = {2017},
}



@inproceedings{kharrat_recommendation_2016,
	title = {Recommendation system based contextual analysis of {Facebook} comment},
	url = {http://dx.doi.org/10.1109/AICCSA.2016.7945792},
	doi = {10.1109/AICCSA.2016.7945792},
	abstract = {This paper present a new recommendation algorithm based on contextual
analysis and new measurements. Social Network is one of the most popular
Web 2.0 applications and related services, like Facebook, have evolved
into a practical means for sharing opinions. Consequently, Social Network
web sites have since become rich data sources for opinion mining. This
paper proposes to introduce external resource from comments posted by
users to predict recommendation and relieve the cold start problem. The
novelty of the proposed approach is that posts are not simply
characterized by an opinion score, as is the case with machine
learning-based classifiers, but instead receive an opinion grade for each
distinct notion in the post. Our approach has been implemented with Java
and Lenskit framework; the study we have conducted on a movie dataset has
shown competitive results. We compared our algorithm to SVD and Slope One
algorithms. We have obtained an improvement of 8\% in precision and recall
as well an improvement of 16\% in RMSE and nDCG.},
	author = {Kharrat, F Ben and Elkhleifi, A and Faiz, R},
	month = nov,
	year = {2016},
	keywords = {Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile},
	pages = {1--6},
}



@inproceedings{schelter_scalable_2012,
	address = {New York, NY, USA},
	title = {Scalable {Similarity}-based {Neighborhood} {Methods} with {MapReduce}},
	url = {http://doi.acm.org/10.1145/2365952.2365984},
	doi = {10.1145/2365952.2365984},
	abstract = {Similarity-based neighborhood methods, a simple and popular approach to
collaborative filtering, infer their predictions by finding users with
similar taste or items that have been similarly rated. If the number of
users grows to millions, the standard approach of sequentially examining
each item and looking at all interacting users does not scale. To solve
this problem, we develop a MapReduce algorithm for the pairwise item
comparison and top-N recommendation problem that scales linearly with
respect to a growing number of users. This parallel algorithm is able to
work on partitioned data and is general in that it supports a wide range
of similarity measures. We evaluate our algorithm on a large dataset
consisting of 700 million song ratings from Yahoo! Music.},
	urldate = {2015-09-23},
	booktitle = {{RecSys} '12},
	publisher = {ACM},
	author = {Schelter, Sebastian and Boden, Christoph and Markl, Volker},
	year = {2012},
	note = {Journal Abbreviation: RecSys '12},
	pages = {163--170},
}



@inproceedings{chang_using_2015,
	address = {New York, NY, USA},
	title = {Using {Groups} of {Items} for {Preference} {Elicitation} in {Recommender} {Systems}},
	url = {http://doi.acm.org/10.1145/2675133.2675210},
	doi = {10.1145/2675133.2675210},
	abstract = {To achieve high quality initial personalization, recommender systems must
provide an efficient and effective process for new users to express their
preferences. We propose that this goal is best served not by the classical
method where users begin by expressing preferences for individual items -
this process is an inefficient way to convert a user's effort into
improved personalization. Rather, we propose that new users can begin by
expressing their preferences for groups of items. We test this idea by
designing and evaluating an interactive process where users express
preferences across groups of items that are automatically generated by
clustering algorithms. We contribute a strategy for recommending items
based on these preferences that is generalizable to any collaborative
filtering-based system. We evaluate our process with both offline
simulation methods and an online user experiment. We find that, as
compared with a baseline rate-15-items interface, (a) users are able to
complete the preference elicitation process in less than half the time,
and (b) users are more satisfied with the resulting recommended items. Our
evaluation reveals several advantages and other trade-offs involved in
moving from item-based preference elicitation to group-based preference
elicitation.},
	urldate = {2015-09-19},
	booktitle = {{CSCW} '15},
	publisher = {ACM},
	author = {Chang, Shuo and Harper, F Maxwell and Terveen, Loren},
	year = {2015},
	note = {Journal Abbreviation: CSCW '15},
	pages = {1258--1269},
}



@inproceedings{ekstrand_user_2014,
	address = {New York, NY, USA},
	title = {User {Perception} of {Differences} in {Movie} {Recommendation} {Algorithms}},
	url = {http://dx.doi.org/10.1145/2645710.2645737},
	doi = {10.1145/2645710.2645737},
	abstract = {Recommender systems research is often based on comparisons of predictive
accuracy: the better the evaluation scores, the better the recommender.
However, it is difficult to compare results from different recommender
systems due to the many options in design and implementation of an
evaluation strategy. Additionally, algorithm implementations can diverge
from the standard formulation due to manual tuning and modifications that
work better in some situations. In this work we compare common
recommendation algorithms as implemented in three popular recommendation
frameworks. To provide a fair comparison, we have complete control of the
evaluation dimensions being benchmarked: dataset, data splitting,
evaluation strategies, and metrics. We also include results using the
internal evaluation mechanisms of these frameworks. Our analysis points to
large differences in recommendation accuracy across frameworks and
strategies, i.e. the same baselines may perform orders of magnitude better
or worse across frameworks. Our results show the necessity of clear
guidelines when reporting evaluation of recommender systems to ensure
reproducibility and comparison of results.},
	booktitle = {{RecSys} '14},
	publisher = {ACM},
	author = {Ekstrand, Michael D and Harper, F Maxwell and Willemsen, Martijn C and Konstan, Joseph A},
	month = oct,
	year = {2014},
	note = {Journal Abbreviation: RecSys '14},
	pages = {161--168},
}



@inproceedings{kluver_evaluating_2014,
	title = {Evaluating {Recommender} {Behavior} for {New} {Users}},
	url = {http://dx.doi.org/10.1145/2645710.2645742},
	doi = {10.1145/2645710.2645742},
	publisher = {ACM},
	author = {Kluver, Daniel and Konstan, Joseph A},
	month = oct,
	year = {2014},
}



@article{de_nart_personalized_2014,
	title = {A {Personalized} {Concept}-driven {Recommender} {System} for {Scientific} {Libraries}},
	volume = {38},
	issn = {1877-0509},
	url = {http://www.sciencedirect.com/science/article/pii/S1877050914013751},
	doi = {10.1016/j.procs.2014.10.015},
	abstract = {Recommender Systems can greatly enhance the exploitation of large digital
libraries; however, in order to achieve good accuracy with collaborative
recommenders some domain assumptions must be met, such as having a large
number of users sharing similar interests over time. Such assumptions may
not hold in digital libraries, where users are structured in relatively
small groups of experts whose interests may change in unpredictable ways:
this is the case of scientific and technical documents archives. Moreover,
when recommending documents, users often expect insights on the
recommended content as well as a detailed explanation of why the system
has selected it, which cannot be provided by collaborative techniques. In
this paper we consider the domain of scientific publications repositories
and propose a content-based recommender based upon a graph representation
of concepts built up by linked keyphrases. This recommender is coupled
with a keyphrase extraction system able to generate meaningful metadata
for the documents, which are the basis for providing helpful and
explainable recommendations.},
	urldate = {2015-09-23},
	journal = {Procedia Comput. Sci.},
	author = {De Nart, D and Tasso, C},
	year = {2014},
	pages = {84--91},
}



@inproceedings{nguyen_improving_2014,
	address = {New York, NY, USA},
	title = {Improving {Recommender} {Systems}: {User} {Roles} and {Lifecycles}},
	url = {http://doi.acm.org/10.1145/2645710.2653363},
	doi = {10.1145/2645710.2653363},
	abstract = {In the era of big data, it is usually agreed that the more data we have,
the better results we can get. However, for some domains that heavily
depend on user inputs (such as recommender systems), the performance
evaluation metrics are sensitive to the amount of noise introduced by
users. Such noise can be from users who only wanted to explore the
systems, and thus did not spend efforts to provide accurate inputs. Noise
can also be introduced by the methods of collecting user ratings. In my
dissertation, I study how user data can affect prediction accuracies and
performances of recommendation algorithms. To that end, I investigate how
the data collection methods and the life cycles of users affect the
prediction accuracies and the performance of recommendation algorithms.},
	urldate = {2015-09-23},
	booktitle = {{RecSys} '14},
	publisher = {ACM},
	author = {Nguyen, Tien T},
	year = {2014},
	note = {Journal Abbreviation: RecSys '14},
	pages = {417--420},
}



@inproceedings{magnuson_event_2015,
	address = {New York, NY, USA},
	title = {Event {Recommendation} {Using} {Twitter} {Activity}},
	url = {http://doi.acm.org/10.1145/2792838.2796556},
	doi = {10.1145/2792838.2796556},
	abstract = {User interactions with Twitter (social network) frequently take place on
mobile devices - a user base that it strongly caters to. As much of
Twitter's traffic comes with geo-tagging information associated with it,
it is a natural platform for geographic recommendations. This paper
proposes an event recommender system for Twitter users, which identifies
twitter activity co-located with previous events, and uses it to drive
geographic recommendations via item-based collaborative filtering.},
	urldate = {2015-09-19},
	booktitle = {{RecSys} '15},
	publisher = {ACM},
	author = {Magnuson, Axel and Dialani, Vijay and Mallela, Deepa},
	year = {2015},
	note = {Journal Abbreviation: RecSys '15},
	pages = {331--332},
}



@inproceedings{zhao_privacy-aware_2014,
	address = {ICST, Brussels, Belgium, Belgium},
	title = {Privacy-aware {Location} {Privacy} {Preference} {Recommendations}},
	url = {http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017},
	doi = {10.4108/icst.mobiquitous.2014.258017},
	abstract = {Location-Based Services have become increasingly popular due to the
prevalence of smart devices and location-sharing applications such as
Facebook and Foursquare. The protection of people's sensitive location
data in such applications is an important requirement. Conventional
location privacy protection methods, however, such as manually defining
privacy rules or asking users to make decisions each time they enter a new
location may be overly complex, intrusive or unwieldy. An alternative is
to use machine learning to predict people's privacy preferences and
automatically configure settings. Model-based machine learning classifiers
may be too computationally complex to be used in real-world applications,
or suffer from poor performance when training data are insufficient. In
this paper we propose a location-privacy recommender that can provide
people with recommendations of appropriate location privacy settings
through user-user collaborative filtering. Using a real-world
location-sharing dataset, we show that the prediction accuracy of our
scheme (73.08\%) is similar to the best performance of model-based
classifiers (75.30\%), and at the same time causes fewer privacy leaks
(11.75\% vs 12.70\%). Our scheme further outperforms model-based classifiers
when there are insufficient training data. Since privacy preferences are
innately private, we make our recommender privacy-aware by obfuscating
people's preferences. Our results show that obfuscation leads to a minimal
loss of prediction accuracy (0.76\%).},
	urldate = {2015-09-23},
	booktitle = {{MOBIQUITOUS} '14},
	publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},
	author = {Zhao, Yuchen and Ye, Juan and Henderson, Tristan},
	year = {2014},
	note = {Journal Abbreviation: MOBIQUITOUS '14},
	pages = {120--129},
}



@article{ghoshal_recommendations_2015,
	title = {Recommendations {Using} {Information} from {Multiple} {Association} {Rules}: {A} {Probabilistic} {Approach}},
	volume = {26},
	issn = {1047-7047},
	url = {http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583},
	doi = {10.1287/isre.2015.0583},
	abstract = {Business analytics has evolved from being a novelty used by a select few
to an accepted facet of conducting business. Recommender systems form a
critical component of the business analytics toolkit and, by enabling
firms to effectively target customers with products and services, are
helping alter the e-commerce landscape. A variety of methods exist for
providing recommendations, with collaborative filtering, matrix
factorization, and association-rule-based methods being the most common.
In this paper, we propose a method to improve the quality of
recommendations made using association rules. This is accomplished by
combining rules when possible and stands apart from existing
rule-combination methods in that it is strongly grounded in probability
theory. Combining rules requires the identification of the best
combination of rules from the many combinations that might exist, and we
use a maximum-likelihood framework to compare alternative combinations.
Because it is impractical to apply the maximum likelihood framework
directly in real time, we show that this problem can equivalently be
represented as a set partitioning problem by translating it into an
information theoretic context—the best solution corresponds to the set of
rules that leads to the highest sum of mutual information associated with
the rules. Through a variety of experiments that evaluate the quality of
recommendations made using the proposed approach, we show that (i) a
greedy heuristic used to solve the maximum likelihood estimation problem
is very effective, providing results comparable to those from using the
optimal set partitioning solution; (ii) the recommendations made by our
approach are more accurate than those made by a variety of
state-of-the-art benchmarks, including collaborative filtering and matrix
factorization; and (iii) the recommendations can be made in a fraction of
a second on a desktop computer, making it practical to use in real-world
applications.},
	number = {3},
	urldate = {2015-09-19},
	journal = {Information Systems Research},
	author = {Ghoshal, Abhijeet and Menon, Syam and Sarkar, Sumit},
	month = jul,
	year = {2015},
	pages = {532--551},
}



@article{guimera_predicting_2012,
	title = {Predicting {Human} {Preferences} {Using} the {Block} {Structure} of {Complex} {Social} {Networks}},
	volume = {7},
	url = {http://dx.doi.org/10.1371/journal.pone.0044620},
	doi = {10.1371/journal.pone.0044620},
	abstract = {With ever-increasing available data, predicting individuals' preferences
and helping them locate the most relevant information has become a
pressing need. Understanding and predicting preferences is also important
from a fundamental point of view, as part of what has been called a “new”
computational social science. Here, we propose a novel approach based on
stochastic block models, which have been developed by sociologists as
plausible models of complex networks of social interactions. Our model is
in the spirit of predicting individuals' preferences based on the
preferences of others but, rather than fitting a particular model, we rely
on a Bayesian approach that samples over the ensemble of all possible
models. We show that our approach is considerably more accurate than
leading recommender algorithms, with major relative improvements between
38\% and 99\% over industry-level algorithms. Besides, our approach sheds
light on decision-making processes by identifying groups of individuals
that have consistently similar preferences, and enabling the analysis of
the characteristics of those groups.},
	number = {9},
	urldate = {2014-10-04},
	journal = {PLoS One},
	author = {Guimerà, Roger and Llorente, Alejandro and Moro, Esteban and Sales-Pardo, Marta},
	month = sep,
	year = {2012},
	pages = {e44620},
}



@inproceedings{wischenbart_recommender_2015,
	title = {Recommender {Systems} for the {People} — {Enhancing} {Personalization} in {Web} {Augmentation}},
	author = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Wimmer, Manuel},
	month = sep,
	year = {2015},
}



@incollection{chowdhury_boostmf_2015,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {{BoostMF}: {Boosted} {Matrix} {Factorisation} for {Collaborative} {Ranking}},
	isbn = {978-3-319-23524-0},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1},
	urldate = {2015-09-19},
	booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},
	publisher = {Springer International Publishing},
	author = {Chowdhury, Nipa and Cai, Xiongcai and Luo, Cheng},
	editor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Costa, Vítor Santos and Gama, João and Jorge, Alípio and Soares, Carlos},
	month = sep,
	year = {2015},
	pages = {3--18},
}



@incollection{kille_stream-based_2015,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Stream-{Based} {Recommendations}: {Online} and {Offline} {Evaluation} as a {Service}},
	isbn = {978-3-319-24026-8},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48},
	urldate = {2015-09-19},
	booktitle = {Experimental {IR} {Meets} {Multilinguality}, {Multimodality}, and {Interaction}},
	publisher = {Springer International Publishing},
	author = {Kille, Benjamin and Lommatzsch, Andreas and Turrin, Roberto and Serény, András and Larson, Martha and Brodt, Torben and Seiler, Jonas and Hopfgartner, Frank},
	editor = {Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, Gareth J F and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola},
	year = {2015},
	pages = {497--517},
}



@phdthesis{ek_recommender_2015,
	address = {Gothenburg, Sweden},
	title = {Recommender {Systems}; {Contextual} {Multi}-{Armed} {Bandit} {Algorithms} for the purpose of targeted advertisement within e-commerce},
	url = {http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf},
	school = {Chalmers University of Technology},
	author = {Ek, Frederik and Stigsson, Robert},
	year = {2015},
}



@article{christou_amore_2015,
	title = {{AMORE}: design and implementation of a commercial-strength parallel hybrid movie recommendation engine},
	issn = {0219-1377},
	url = {http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z},
	doi = {10.1007/s10115-015-0866-z},
	urldate = {2015-09-19},
	journal = {Knowl. Inf. Syst.},
	author = {Christou, Ioannis T and Amolochitis, Emmanouil and Tan, Zheng-Hua},
	month = aug,
	year = {2015},
	pages = {1--26},
}



@inproceedings{cao_towards_2015,
	title = {Towards {Making} {Systems} {Forget} with {Machine} {Unlearning}},
	url = {http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf},
	abstract = {Today’s systems produce a wealth of data every day, and the data further
generates more data, i.e., the derived data, forming into a complex data
propagation network, defined as the data’s lineage. There are many reasons
for users and administrators to forget certain data including the data’s
lineage. From the privacy perspective, a system may leak private
information of certain users, and those users unhappy about privacy leaks
naturally want to forget their data and its lineage. From the security
perspective, an anomaly detection system can be polluted by adversaries
through injecting manually crafted data into the training set. Therefore,
we envision forgetting systems, capable of completely forgetting certain
data and its lineage. In this paper, we focus on making learning systems
forget, the process of which is defined as machine unlearning or
unlearning. To perform unlearning upon learning system, we present general
unlearning criteria, i.e., converting a learning system or part of it into
a summation form of statistical query learning model, and updating all the
summations to achieve unlearning. Then, we integrate our unlearning
criteria into an unlearning architecture that interacts with all the
components of a learning system, such as sample clustering and feature
selection. To demonstrate our unlearning criteria and architecture, we
select four real-world learning systems, including an item-item
recommendation system, an online social network spam filter, and a malware
detection system. These systems are first exposed to an adversarial
environment, e.g., if the system is potentially vulnerable to training
data pollution, we first pollute the training data set and show that the
detection rate drops significantly. Then, we apply our unlearning
technique upon those affected systems, either polluted or leaking private
information. Our results show that after unlearning, the detection rate of
a polluted system increases back to the one before pollution, and a system
leaking a particular user’s private information completely forgets that
information.},
	publisher = {IEEE},
	author = {Cao, Yinzhi and Yang, Junfeng},
	month = may,
	year = {2015},
}



@article{amolochitis_implementing_2014,
	title = {Implementing a {Commercial}-{Strength} {Parallel} {Hybrid} {Movie} {Recommendation} {Engine}},
	volume = {29},
	issn = {1541-1672},
	url = {http://dx.doi.org/10.1109/MIS.2014.23},
	doi = {10.1109/MIS.2014.23},
	abstract = {AMORE is a hybrid recommendation system that provides movie
recommendations for a major triple-play services provider in Greece.
Combined with our own implementations of several user-, item-, and
content-based recommendation algorithms, AMORE significantly outperforms
other state-of-the-art implementations both in solution quality and
response time. AMORE currently serves daily recommendation requests for
all active subscribers of the provider's video-on-demand services and has
contributed to an increase of rental profits and customer retention.},
	number = {2},
	journal = {IEEE Intell. Syst.},
	author = {Amolochitis, E and Christou, I T and Tan, Zheng-Hua},
	month = mar,
	year = {2014},
	pages = {92--96},
}



@inproceedings{elkhelifi_recommendation_2015,
	title = {Recommendation {Systems} {Based} on {Online} {User}'s {Action}},
	url = {http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69},
	doi = {10.1109/CIT/IUCC/DASC/PICOM.2015.69},
	abstract = {In this paper, we propose a new recommender algorithm based on
multi-dimensional users behavior and new measurements. It's used in the
framework of our recommender system that use knowledge discovery
techniques to the problem of making product recommendations during a live
user interaction. Most of Collaborative filtering algorithms based on
user's rating or similar item that other users bought, we propose to
combine all user's action to predict recommendation. These systems are
achieving widespread success in E-tourism nowadays. We evaluate our
algorithm on tourism dataset. Evaluations have shown good results. We
compared our algorithm to Slope One and Weight Slope One. We obtained an
improvement of 5\% in precision and recall. And an improvement of 12\% in
RMSE and nDCG.},
	author = {Elkhelifi, A and Kharrat, F Ben and Faiz, R},
	month = oct,
	year = {2015},
	pages = {485--490},
}



@phdthesis{salam_patrous_evaluating_2016,
	address = {Stockholm, Sweden},
	title = {Evaluating {Prediction} {Accuracy} for {Collaborative} {Filtering} {Algorithms} in {Recommender} {Systems}},
	url = {http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&c=1&af=%5B%5D&searchType=LIST_LATEST&query=&language=en&pid=diva2%3A927356&aq=%5B%5B%5D%5D&sf=all&aqe=%5B%5D&sortOrder=author_sort_asc&onlyFullText=false&noOfRows=50&dswid=-7195},
	abstract = {Recommender systems are a relatively new technology that is commonly used
by e-commerce websites and streaming services among others, to predict
user opinion about products. This report studies two ...},
	urldate = {2016-06-13},
	school = {KTH Royal Institute of Technology},
	author = {Salam Patrous, Ziad and Najafi, Safir},
	year = {2016},
}



@phdthesis{chang_leveraging_2016,
	address = {Minneapolis, MN, USA},
	title = {Leveraging {Collective} {Intelligence} in {Recommender} {System}},
	url = {http://hdl.handle.net/11299/182725},
	abstract = {Recommender systems, since their introduction 20 years ago, have been
widely deployed in web services to alleviate user information overload.
Driven by business objectives of their applications, the focus of
recommender systems has shifted from accurately modeling and predicting
user preferences to offering good personalized user experience. The later
is difficult because there are many factors, e.g., tenure of a user,
context of recommendation and transparency of recommender system, that
affect users' perception of recommendations. Many of these factors are
subjective and not easily quantifiable, posing challenges to recommender
algorithms. When pure algorithmic solutions are at their limits in
providing good user experience in recommender systems, we turn to the
collective intelligence of human and computer. Computer and human are
complementary to each other: computers are fast at computation and data
processing and have accurate memory; humans are capable of complex
reasoning, being creative and relating to other humans. In fact, such
close collaborations between human and computer have precedent: after
chess master Garry Kasparov lost to IBM computer ``Deep Blue'', he invited
a new form of chess --- advanced chess, in which human player and a
computer program teams up against such pairs. In this thesis, we leverage
the collective intelligence of human and computer to tackle several
challenges in recommender systems and demonstrate designs of such hybrid
systems. We make contributions to the following aspects of recommender
systems: providing better new user experience, enhancing topic modeling
component for items, composing better recommendation sets and generating
personalized natural language explanations. These four applications
demonstrate different ways of designing systems with collective
intelligence, applicable to domains other than recommender systems. We
believe the collective intelligence of human and computer can power more
intelligent, user friendly and creative systems, worthy of continuous
research effort in future.},
	urldate = {2016-11-01},
	school = {University of Minnesota},
	author = {Chang, Shuo},
	month = aug,
	year = {2016},
}



@article{pessemier_hybrid_2016,
	title = {Hybrid group recommendations for a travel service},
	volume = {75},
	issn = {1380-7501},
	url = {http://link.springer.com/article/10.1007/s11042-016-3265-x},
	doi = {10.1007/s11042-016-3265-x},
	abstract = {Recommendation techniques have proven their usefulness as a tool to cope
with the information overload problem in many classical domains such as
movies, books, and music. Additional challenges for recommender systems
emerge in the domain of tourism such as acquiring metadata and feedback,
the sparsity of the rating matrix, user constraints, and the fact that
traveling is often a group activity. This paper proposes a recommender
system that offers personalized recommendations for travel destinations to
individuals and groups. These recommendations are based on the users’
rating profile, personal interests, and specific demands for their next
destination. The recommendation algorithm is a hybrid approach combining a
content-based, collaborative filtering, and knowledge-based solution. For
groups of users, such as families or friends, individual recommendations
are aggregated into group recommendations, with an additional opportunity
for users to give feedback on these group recommendations. A group of test
users evaluated the recommender system using a prototype web application.
The results prove the usefulness of individual and group recommendations
and show that users prefer the hybrid algorithm over each individual
technique. This paper demonstrates the added value of various
recommendation algorithms in terms of different quality aspects, compared
to an unpersonalized list of the most-popular destinations.},
	number = {5},
	urldate = {2016-03-11},
	journal = {Multimed. Tools Appl.},
	author = {Pessemier, Toon De and Dhondt, Jeroen and Martens, Luc},
	month = jan,
	year = {2016},
	pages = {1--25},
}



@phdthesis{nguyen_enhancing_2016,
	address = {Minneapolis, MN, USA},
	title = {Enhancing {User} {Experience} {With} {Recommender} {Systems} {Beyond} {Prediction} {Accuracies}},
	url = {http://hdl.handle.net/11299/182780},
	abstract = {In this dissertation, we examine to improve the user experience with
recommender systems beyond prediction accuracy. We focus on the following
aspects of the user experience. In chapter 3 we examine if a recommender
system exposes users to less diverse contents over time. In chapter 4 we
look at the relationships between user personality and user preferences
for recommendation diversity, popularity, and serendipity. In chapter 5 we
investigate the relations between the self-reported user satisfaction and
the three recommendation properties with the inferred user recommendation
consumption. In chapter 6 we look at four different rating inter- faces
and evaluated how these interfaces affected the user rating experience. We
find that over time a recommender system exposes users to less-diverse
contents and that users rate less-diverse items. However, users who took
recommendations were exposed to more diverse recommendations than those
who did not. Furthermore, users with different personalities have
different preferences for recommendation diversity, popularity, and
serendipity (e.g. some users prefer more diverse recommendations, while
others prefer similar ones). We also find that user satisfaction with
recommendation popularity and serendipity measured with survey questions
strongly relate to user recommendation consumption inferred with logged
data. We then propose a way to get better signals about user preferences
and help users rate items in the recommendation systems more consistently.
That is providing exemplars to users at the time they rate the items
improved the consistency of users’ ratings. Our results suggest several
ways recommender system practitioners and re- searchers can enrich the
user experience. For example, by integrating users’ personality into
recommendation frameworks, we can help recommender systems deliver
recommendations with the preferred levels of diversity, popularity, and
serendipity to individual users. We can also facilitate the rating process
by integrating a set of proven rating-support techniques into the systems’
interfaces.},
	urldate = {2016-11-01},
	school = {University of Minnesota},
	author = {Nguyen, Tien},
	month = aug,
	year = {2016},
}



@article{benjamin_heitmann_technical_2013,
	title = {Technical {Report} on evaluation of recommendations generated by spreading activation},
	url = {http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation},
	author = {Benjamin Heitmann, Conor Hayes},
	year = {2013},
}



@inproceedings{dragovic_exploiting_2015,
	title = {Exploiting {Reviews} to {Guide} {Users}’ {Selections}},
	url = {http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf},
	urldate = {2017-03-01},
	author = {Dragovic, Nevena and Pera, Maria Soledad},
	year = {2015},
}



@inproceedings{larrain_towards_2015,
	title = {Towards {Improving} {Top}-{N} {Recommendation} by {Generalization} of {SLIM}},
	url = {http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf},
	author = {Larraín, Santiago and Parra, Denis and Soto, Alvaro},
	year = {2015},
}



@phdthesis{dhondt_hybrid_2015,
	address = {Gent, Belgium},
	title = {A hybrid group recommender system for travel destinations},
	school = {University of Gent},
	author = {Dhondt, Jeroen},
	month = may,
	year = {2015},
}



@misc{noauthor_machine_2016,
	title = {Machine ‘{Unlearning}’ {Technique} {Wipes} {Out} {Unwanted} {Data} {Quickly} and {Completely}},
	url = {http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely},
	abstract = {Cao and Yang believe that easy adoption of forgetting systems will be
increasingly in demand. The pair has developed a way to do it faster and
more effectively than what is currently available. Their concept, called
"machine unlearning," is so promising that the duo have been awarded a
four-year, \$1.2 million National Science Foundation grant — split between
Lehigh and Columbia — to develop the approach.},
	urldate = {2016-03-16},
	month = mar,
	year = {2016},
}



@article{de_pessemier_heart_2018,
	title = {Heart rate monitoring, activity recognition, and recommendation for e-coaching},
	issn = {1380-7501},
	url = {https://link.springer.com/article/10.1007/s11042-018-5640-2},
	doi = {10.1007/s11042-018-5640-2},
	abstract = {Equipped with hardware, such as accelerometer and heart rate sensor,
wearables enable measuring physical activities and heart rate. However,
the accuracy of these heart rate measurements is still unclear and the
coupling with activity recognition is often missing in health apps. This
study evaluates heart rate monitoring with four different device types: a
specialized sports device with chest strap, a fitness tracker, a smart
watch, and a smartphone using photoplethysmography. In a state of rest,
similar measurement results are obtained with the four devices. During
physical activities, the fitness tracker, smart watch, and smartphone
measure sudden variations in heart rate with a delay, due to movements of
the wrist. Moreover, this study showed that physical activities, such as
squats and dumbbell curl, can be recognized with fitness trackers. By
combining heart rate monitoring and activity recognition, personal
suggestions for physical activities are generated using a tag-based
recommender and rule-based filter.},
	urldate = {2018-02-08},
	journal = {Multimed. Tools Appl.},
	author = {De Pessemier, Toon and Martens, Luc},
	month = jan,
	year = {2018},
	note = {Publisher: Springer US},
	pages = {1--18},
}
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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2211.14219\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022', 'http://arxiv.org/abs/2211.14219', event);\">\n    The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Aridor, G.; Goncalves, D.; Kluver, D.; Kong, R.;  and Konstan, J.\n</span>\n\n  \n\n</span>\n\n  November 2022.\n  <span class=\"note\">ISBN: 2211.14219 Publication Title: arXiv [econ.GN]</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2211.14219\"\n     onclick=\"log_download('aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022', 'http://arxiv.org/abs/2211.14219', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('aridor-goncalves-kluver-kong-konstan-theeconomicsofrecommendersystemsevidencefromafieldexperimentonmovielens-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@unpublished{aridor_economics_2022,\n\ttitle = {The {Economics} of {Recommender} {Systems}: {Evidence} from a {Field} {Experiment} on {MovieLens}},\n\turl = {http://arxiv.org/abs/2211.14219},\n\tabstract = {We conduct a field experiment on a movie-recommendation platform to\nidentify if and how recommendations affect consumption. We use\nwithin-consumer randomization at the good level and elicit beliefs about\nunconsumed goods to disentangle exposure from informational effects. We\nfind recommendations increase consumption beyond its role in exposing\ngoods to consumers. We provide support for an informational mechanism:\nrecommendations affect consumers&#x27; beliefs, which in turn explain\nconsumption. Recommendations reduce uncertainty about goods consumers are\nmost uncertain about and induce information acquisition. Our results\nhighlight the importance of recommender systems&#x27; informational role when\nconsidering policies targeting these systems in online marketplaces.},\n\tauthor = {Aridor, Guy and Goncalves, Duarte and Kluver, Daniel and Kong, Ruoyan and Konstan, Joseph},\n\tmonth = nov,\n\tyear = {2022},\n\tnote = {ISBN: 2211.14219\nPublication Title: arXiv [econ.GN]},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations increase consumption beyond its role in exposing goods to consumers. We provide support for an informational mechanism: recommendations affect consumers' beliefs, which in turn explain consumption. Recommendations reduce uncertainty about goods consumers are most uncertain about and induce information acquisition. Our results highlight the importance of recommender systems' informational role when considering policies targeting these systems in online marketplaces.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021', 'http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf', event);\">\n    Hybrid Recommender for Research Papers and Articles.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ibrahim, A. J.; Zira, P.;  and Abdulganiyyi, N.\n</span>\n\n  \n\n</span>\n\n  <i>Int. J. Intell. Inf. Database Syst.</i>, 10(2): 9.  2021.\n  <span class=\"note\">Publisher: Science Publishing Group</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf\"\n     onclick=\"log_download('ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021', 'http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Hybrid Recommender for Research Papers and Articles [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ibrahim-zira-abdulganiyyi-hybridrecommenderforresearchpapersandarticles-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ibrahim_hybrid_2021,\n\ttitle = {Hybrid {Recommender} for {Research} {Papers} and {Articles}},\n\tvolume = {10},\n\turl = {http://article.ijoiis.com/pdf/10.11648.j.ijiis.20211002.11.pdf},\n\tabstract = {… GroupLens called LensKit , along with set of tools for such system was\nused to implement Collaborative filtering algorithm. This research uses\nonly the LensKit -core and LensKit -data-structures modules to implement\nthis section of the algorithm …},\n\tnumber = {2},\n\tjournal = {Int. J. Intell. Inf. Database Syst.},\n\tauthor = {Ibrahim, Alhassan Jamilu and Zira, Peter and Abdulganiyyi, Nuraini},\n\tyear = {2021},\n\tnote = {Publisher: Science Publishing Group},\n\tpages = {9},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  … GroupLens called LensKit , along with set of tools for such system was used to implement Collaborative filtering algorithm. This research uses only the LensKit -core and LensKit -data-structures modules to implement this section of the algorithm …\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1145/3463274.3463951\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021', 'https://doi.org/10.1145/3463274.3463951', event);\">\n    Recommender Systems for Software Project Managers.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wei, L.;  and Capretz, L. F.\n</span>\n\n  \n\n</span>\n\n  In <i>EASE 2021</i>, pages 412–417, New York, NY, USA, June 2021. Association for Computing Machinery\n  <span class=\"note\">Journal Abbreviation: EASE 2021</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1145/3463274.3463951\"\n     onclick=\"log_download('wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021', 'https://doi.org/10.1145/3463274.3463951', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommender Systems for Software Project Managers [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3463274.3463951\"\n     onclick=\"log_download('wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021', 'http://doi.org/10.1145/3463274.3463951', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wei-capretz-recommendersystemsforsoftwareprojectmanagers-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{wei_recommender_2021,\n\taddress = {New York, NY, USA},\n\ttitle = {Recommender {Systems} for {Software} {Project} {Managers}},\n\turl = {https://doi.org/10.1145/3463274.3463951},\n\tdoi = {10.1145/3463274.3463951},\n\tabstract = {The design of recommendation systems is based on complex information\nprocessing and big data interaction. This personalized view has evolved\ninto a hot area in the past decade, where applications might have been\nproved to help for solving problem in the software development field.\nTherefore, with the evolvement of Recommendation System in Software\nEngineering (RSSE), the coordination of software projects with their\nstakeholders is improving. This experiment examines four open source\nrecommender systems and implemented a customized recommender engine with\ntwo industrial-oriented packages: Lenskit and Mahout. Each of the main\nfunctions was examined and issues were identified during the experiment.},\n\turldate = {2021-09-14},\n\tbooktitle = {{EASE} 2021},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Wei, Liang and Capretz, Luiz Fernando},\n\tmonth = jun,\n\tyear = {2021},\n\tnote = {Journal Abbreviation: EASE 2021},\n\tkeywords = {RSSE, Recommender Engine, Project Management, Recommendation System, Recommendation System in Software Engineering},\n\tpages = {412--417},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The design of recommendation systems is based on complex information processing and big data interaction. This personalized view has evolved into a hot area in the past decade, where applications might have been proved to help for solving problem in the software development field. Therefore, with the evolvement of Recommendation System in Software Engineering (RSSE), the coordination of software projects with their stakeholders is improving. This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout. Each of the main functions was examined and issues were identified during the experiment.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021', 'http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf', event);\">\n    Privacy and performance in recommender systems: Exploration of potential influence of CCPA.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhou, M.; Song, Y.;  and Adomavicius, G.\n</span>\n\n  \n\n</span>\n\n  In  2021. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf\"\n     onclick=\"log_download('zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021', 'http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Privacy and performance in recommender systems: Exploration of potential influence of CCPA [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhou-song-adomavicius-privacyandperformanceinrecommendersystemsexplorationofpotentialinfluenceofccpa-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{zhou_privacy_2021,\n\ttitle = {Privacy and performance in recommender systems: {Exploration} of potential influence of {CCPA}},\n\turl = {http://2021.cswimworkshop.org/wp-content/uploads/2021/06/cswim2021_paper_80.pdf},\n\turldate = {2021-07-12},\n\tauthor = {Zhou, Meizi and Song, Yicheng and Adomavicius, Gediminas},\n\tyear = {2021},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s11042-020-09803-8\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021', 'https://doi.org/10.1007/s11042-020-09803-8', event);\">\n    Engaging end-user driven recommender systems: personalization through web augmentation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wischenbart, M.; Firmenich, S.; Rossi, G.; Bosetti, G.;  and Kapsammer, E.\n</span>\n\n  \n\n</span>\n\n  <i>Multimed. Tools Appl.</i>, 80(5): 6785–6809. February 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s11042-020-09803-8\"\n     onclick=\"log_download('wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021', 'https://doi.org/10.1007/s11042-020-09803-8', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Engaging end-user driven recommender systems: personalization through web augmentation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11042-020-09803-8\"\n     onclick=\"log_download('wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021', 'http://doi.org/10.1007/s11042-020-09803-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wischenbart-firmenich-rossi-bosetti-kapsammer-engagingenduserdrivenrecommendersystemspersonalizationthroughwebaugmentation-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{wischenbart_engaging_2021,\n\ttitle = {Engaging end-user driven recommender systems: personalization through web augmentation},\n\tvolume = {80},\n\tissn = {1380-7501},\n\turl = {https://doi.org/10.1007/s11042-020-09803-8},\n\tdoi = {10.1007/s11042-020-09803-8},\n\tabstract = {In the past decades recommender systems have become a powerful tool to\nimprove personalization on the Web. Yet, many popular websites lack such\nfunctionality, its implementation usually requires certain technical\nskills, and, above all, its introduction is beyond the scope and control\nof end-users. To alleviate these problems, this paper presents a novel\ntool to empower end-users without programming skills, without any\ninvolvement of website providers, to embed personalized recommendations of\nitems into arbitrary websites on client-side. For this we have developed a\ngeneric meta-model to capture recommender system configuration parameters\nin general as well as in a web augmentation context. Thereupon, we have\nimplemented a wizard in the form of an easy-to-use browser plug-in,\nallowing the generation of so-called user scripts, which are executed in\nthe browser to engage collaborative filtering functionality from a\nprovided external rest service. We discuss functionality and limitations\nof the approach, and in a study with end-users we assess the usability and\nshow its suitability for combining recommender systems with web\naugmentation techniques, aiming to empower end-users to implement\ncontrollable recommender applications for a more personalized browsing\nexperience.},\n\tnumber = {5},\n\tjournal = {Multimed. Tools Appl.},\n\tauthor = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Bosetti, Gabriela and Kapsammer, Elisabeth},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {6785--6809},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://arxiv.org/abs/2102.00482\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021', 'http://arxiv.org/abs/2102.00482', event);\">\n    Improving Accountability in Recommender Systems Research Through Reproducibility.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bellogín, A.;  and Said, A.\n</span>\n\n  \n\n</span>\n\n  January 2021.\n  <span class=\"note\">ISBN: 2102.00482 Publication Title: arXiv [cs.IR]</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://arxiv.org/abs/2102.00482\"\n     onclick=\"log_download('bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021', 'http://arxiv.org/abs/2102.00482', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Improving Accountability in Recommender Systems Research Through Reproducibility [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bellogn-said-improvingaccountabilityinrecommendersystemsresearchthroughreproducibility-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@unpublished{bellogin_improving_2021,\n\ttitle = {Improving {Accountability} in {Recommender} {Systems} {Research} {Through} {Reproducibility}},\n\turl = {http://arxiv.org/abs/2102.00482},\n\tabstract = {Reproducibility is a key requirement for scientific progress. It allows\nthe reproduction of the works of others, and, as a consequence, to fully\ntrust the reported claims and results. In this work, we argue that, by\nfacilitating reproducibility of recommender systems experimentation, we\nindirectly address the issues of accountability and transparency in\nrecommender systems research from the perspectives of practitioners,\ndesigners, and engineers aiming to assess the capabilities of published\nresearch works. These issues have become increasingly prevalent in recent\nliterature. Reasons for this include societal movements around intelligent\nsystems and artificial intelligence striving towards fair and objective\nuse of human behavioral data (as in Machine Learning, Information\nRetrieval, or Human-Computer Interaction). Society has grown to expect\nexplanations and transparency standards regarding the underlying\nalgorithms making automated decisions for and around us. This work surveys\nexisting definitions of these concepts, and proposes a coherent\nterminology for recommender systems research, with the goal to connect\nreproducibility to accountability. We achieve this by introducing several\nguidelines and steps that lead to reproducible and, hence, accountable\nexperimental workflows and research. We additionally analyze several\ninstantiations of recommender system implementations available in the\nliterature, and discuss the extent to which they fit in the introduced\nframework. With this work, we aim to shed light on this important problem,\nand facilitate progress in the field by increasing the accountability of\nresearch.},\n\tauthor = {Bellogín, Alejandro and Said, Alan},\n\tmonth = jan,\n\tyear = {2021},\n\tnote = {ISBN: 2102.00482\nPublication Title: arXiv [cs.IR]},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving towards fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human-Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts, and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature, and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem, and facilitate progress in the field by increasing the accountability of research.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1287/ijoc.2018.0882\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020', 'https://doi.org/10.1287/ijoc.2018.0882', event);\">\n    Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cheng, X.; Zhang, J.;  and Yan, L. (.\n</span>\n\n  \n\n</span>\n\n  <i>INFORMS J. Comput.</i>, 32(2): 303–320. April 2020.\n  <span class=\"note\">Publisher: INFORMS</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1287/ijoc.2018.0882\"\n     onclick=\"log_download('cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020', 'https://doi.org/10.1287/ijoc.2018.0882', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1287/ijoc.2018.0882\"\n     onclick=\"log_download('cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020', 'http://doi.org/10.1287/ijoc.2018.0882', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cheng-zhang-yan-understandingtheimpactofindividualusersratingcharacteristicsonthepredictiveaccuracyofrecommendersystems-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{cheng_understanding_2020,\n\ttitle = {Understanding the {Impact} of {Individual} {Users}’ {Rating} {Characteristics} on the {Predictive} {Accuracy} of {Recommender} {Systems}},\n\tvolume = {32},\n\tissn = {1091-9856},\n\turl = {https://doi.org/10.1287/ijoc.2018.0882},\n\tdoi = {10.1287/ijoc.2018.0882},\n\tabstract = {In this study, we investigate how individual users? rating characteristics\naffect the user-level performance of recommendation algorithms. We measure\nusers? rating characteristics from three perspectives: rating value,\nrating structure, and neighborhood network embeddedness. We study how\nthese three categories of measures influence the predictive accuracy of\npopular recommendation algorithms for each user. Our experiments use five\nreal-world data sets with varying characteristics. For each individual\nuser, we estimate the predictive accuracy of three recommendation\nalgorithms. We then apply regression-based models to uncover the\nrelationships between rating characteristics and recommendation\nperformance at the individual user level. Our experimental results show\nconsistent and significant effects of several rating measures on\nrecommendation accuracy. Understanding how rating characteristics affect\nthe recommendation performance at the individual user level has practical\nimplications for the design of recommender systems.},\n\tnumber = {2},\n\tjournal = {INFORMS J. Comput.},\n\tauthor = {Cheng, Xiaoye and Zhang, Jingjing and Yan, Lu (lucy)},\n\tmonth = apr,\n\tyear = {2020},\n\tnote = {Publisher: INFORMS},\n\tpages = {303--320},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this study, we investigate how individual users? rating characteristics affect the user-level performance of recommendation algorithms. We measure users? rating characteristics from three perspectives: rating value, rating structure, and neighborhood network embeddedness. We study how these three categories of measures influence the predictive accuracy of popular recommendation algorithms for each user. Our experiments use five real-world data sets with varying characteristics. For each individual user, we estimate the predictive accuracy of three recommendation algorithms. We then apply regression-based models to uncover the relationships between rating characteristics and recommendation performance at the individual user level. Our experimental results show consistent and significant effects of several rating measures on recommendation accuracy. Understanding how rating characteristics affect the recommendation performance at the individual user level has practical implications for the design of recommender systems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/s00607-018-0687-5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020', 'http://link.springer.com/10.1007/s00607-018-0687-5', event);\">\n    How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kotkov, D.; Veijalainen, J.;  and Wang, S.\n</span>\n\n  \n\n</span>\n\n  <i>Computing</i>, 102(2): 393–411. February 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/s00607-018-0687-5\"\n     onclick=\"log_download('kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020', 'http://link.springer.com/10.1007/s00607-018-0687-5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s00607-018-0687-5\"\n     onclick=\"log_download('kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020', 'http://doi.org/10.1007/s00607-018-0687-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kotkov-veijalainen-wang-howdoesserendipityaffectdiversityinrecommendersystemsaserendipityorientedgreedyalgorithm-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{kotkov_how_2020,\n\ttitle = {How does serendipity affect diversity in recommender systems? {A} serendipity-oriented greedy algorithm},\n\tvolume = {102},\n\tissn = {0144-3097},\n\turl = {http://link.springer.com/10.1007/s00607-018-0687-5},\n\tdoi = {10.1007/s00607-018-0687-5},\n\tabstract = {Most recommender systems suggest items that are popular among all users\nand similar to items a user usually consumes. As a result, the user\nreceives recommendations that she/he is already familiar with or would\nfind anyway, leading to low satisfaction. To overcome this problem, a\nrecommender system should suggest novel, relevant and unexpected i.e.,\nserendipitous items. In this paper, we propose a serendipity-oriented,\nreranking algorithm called a serendipity-oriented greedy (SOG) algorithm,\nwhich improves serendipity of recommendations through feature\ndiversification and helps overcome the overspecialization problem. To\nevaluate our algorithm, we employed the only publicly available dataset\ncontaining user feedback regarding serendipity. We compared our SOG\nalgorithm with topic diversification, popularity baseline, singular value\ndecomposition, serendipitous personalized ranking and Zheng’s algorithms\nrelying on the above dataset. SOG outperforms other algorithms in terms of\nserendipity and diversity. It also outperforms serendipity-oriented\nalgorithms in terms of accuracy, but underperforms accuracy-oriented\nalgorithms in terms of accuracy. We found that the increase of diversity\ncan hurt accuracy and harm or improve serendipity depending on the size of\ndiversity increase.},\n\tnumber = {2},\n\tjournal = {Computing},\n\tauthor = {Kotkov, Denis and Veijalainen, Jari and Wang, Shuaiqiang},\n\tmonth = feb,\n\tyear = {2020},\n\tpages = {393--411},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available dataset containing user feedback regarding serendipity. We compared our SOG algorithm with topic diversification, popularity baseline, singular value decomposition, serendipitous personalized ranking and Zheng’s algorithms relying on the above dataset. SOG outperforms other algorithms in terms of serendipity and diversity. It also outperforms serendipity-oriented algorithms in terms of accuracy, but underperforms accuracy-oriented algorithms in terms of accuracy. We found that the increase of diversity can hurt accuracy and harm or improve serendipity depending on the size of diversity increase.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"noffsinger-predictiveaccuracyofrecommenderalgorithms-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'noffsinger-predictiveaccuracyofrecommenderalgorithms-2020', 'https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2', event);\">\n    Predictive Accuracy of Recommender Algorithms.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Noffsinger, W. B\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, Nova Southeastern University, Ann Arbor, United States,  2020.\n  <span class=\"note\">Publication Title: Information Systems (DISS)</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2\"\n     onclick=\"log_download('noffsinger-predictiveaccuracyofrecommenderalgorithms-2020', 'https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Predictive Accuracy of Recommender Algorithms [link]\"\n       title=\"Https://libproxy.boisestate.edu/login?url\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Https://libproxy.boisestate.edu/login?url</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/noffsinger-predictiveaccuracyofrecommenderalgorithms-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('noffsinger-predictiveaccuracyofrecommenderalgorithms-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{noffsinger_predictive_2020,\n\taddress = {Ann Arbor, United States},\n\ttitle = {Predictive {Accuracy} of {Recommender} {Algorithms}},\n\turl = {https://libproxy.boisestate.edu/login?url=https://www-proquest-com.libproxy.boisestate.edu/dissertations-theses/predictive-accuracy-recommender-algorithms/docview/2466761384/se-2},\n\tabstract = {Recommender systems present a customized list of items based upon user or\nitem characteristics with the objective of reducing a large number of\npossible choices to a smaller ranked set most likely to appeal to the\nuser. A variety of algorithms for recommender systems have been developed\nand refined including applications of deep learning neural networks.\nRecent research reports point to a need to perform carefully controlled\nexperiments to gain insights about the relative accuracy of different\nrecommender algorithms, because studies evaluating different methods have\nnot used a common set of benchmark data sets, baseline models, and\nevaluation metrics.The dissertation used publicly available sources of\nratings data with a suite of three conventional recommender algorithms and\ntwo deep learning (DL) algorithms in controlled experiments to assess\ntheir comparative accuracy. Results for the non-DL algorithms conformed\nwell to published results and benchmarks. The two DL algorithms did not\nperform as well and illuminated known challenges implementing DL\nrecommender algorithms as reported in the literature. Model overfitting is\ndiscussed as a potential explanation for the weaker performance of the DL\nalgorithms and several regularization strategies are reviewed as possible\napproaches to improve predictive error. Findings justify the need for\nfurther research in the use of deep learning models for recommender\nsystems.},\n\tschool = {Nova Southeastern University},\n\tauthor = {Noffsinger, William B},\n\tcollaborator = {Mukherjee, Sumitra},\n\tyear = {2020},\n\tnote = {Publication Title: Information Systems (DISS)},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems present a customized list of items based upon user or item characteristics with the objective of reducing a large number of possible choices to a smaller ranked set most likely to appeal to the user. A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms, because studies evaluating different methods have not used a common set of benchmark data sets, baseline models, and evaluation metrics.The dissertation used publicly available sources of ratings data with a suite of three conventional recommender algorithms and two deep learning (DL) algorithms in controlled experiments to assess their comparative accuracy. Results for the non-DL algorithms conformed well to published results and benchmarks. The two DL algorithms did not perform as well and illuminated known challenges implementing DL recommender algorithms as reported in the literature. Model overfitting is discussed as a potential explanation for the weaker performance of the DL algorithms and several regularization strategies are reviewed as possible approaches to improve predictive error. Findings justify the need for further research in the use of deep learning models for recommender systems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0950705119304484\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020', 'http://www.sciencedirect.com/science/article/pii/S0950705119304484', event);\">\n    A new similarity measure for collaborative filtering based recommender systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gazdar, A.;  and Hidri, L.\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge-Based Systems</i>, 188: 105058. January 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0950705119304484\"\n     onclick=\"log_download('gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020', 'http://www.sciencedirect.com/science/article/pii/S0950705119304484', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A new similarity measure for collaborative filtering based recommender systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.knosys.2019.105058\"\n     onclick=\"log_download('gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020', 'http://doi.org/10.1016/j.knosys.2019.105058', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gazdar-hidri-anewsimilaritymeasureforcollaborativefilteringbasedrecommendersystems-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{gazdar_new_2020,\n\ttitle = {A new similarity measure for collaborative filtering based recommender systems},\n\tvolume = {188},\n\tissn = {0950-7051},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0950705119304484},\n\tdoi = {10.1016/j.knosys.2019.105058},\n\tabstract = {The objective of a recommender system is to provide customers with\npersonalized recommendations while selecting an item among a set of\nproducts (movies, books, etc.). The collaborative filtering is the most\nused technique for recommender systems. One of the main components of a\nrecommender system based on the collaborative filtering technique, is the\nsimilarity measure used to determine the set of users having the same\nbehavior with regard to the selected items. Several similarity functions\nhave been proposed, with different performances in terms of accuracy and\nquality of recommendations. In this paper, we propose a new simple and\nefficient similarity measure. Its mathematical expression is determined\nthrough the following paper contributions: 1) transforming some intuitive\nand qualitative conditions, that should be satisfied by the similarity\nmeasure, into relevant mathematical equations namely: the integral\nequation, the linear system of differential equations and a non-linear\nsystem and 2) resolving the equations to achieve the kernel function of\nthe similarity measure. The extensive experimental study driven on a\nbenchmark datasets shows that the proposed similarity measure is very\ncompetitive, especially in terms of accuracy, with regards to some\nrepresentative similarity measures of the literature.},\n\tjournal = {Knowledge-Based Systems},\n\tauthor = {Gazdar, Achraf and Hidri, Lotfi},\n\tmonth = jan,\n\tyear = {2020},\n\tkeywords = {Collaborative filtering, Neighborhood based CF, Recommendation systems, Similarity measure},\n\tpages = {105058},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1007/978-3-030-39815-6_16\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020', 'http://dx.doi.org/10.1007/978-3-030-39815-6_16', event);\">\n    Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Polychronou, I.; Katsivelis, P.; Papakonstantinou, M.; Stoitsis, G.;  and Manouselis, N.\n</span>\n\n  \n\n</span>\n\n  In pages 165–172,  2020. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1007/978-3-030-39815-6_16\"\n     onclick=\"log_download('polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020', 'http://dx.doi.org/10.1007/978-3-030-39815-6_16', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate Predictions [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-39815-6_16\"\n     onclick=\"log_download('polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020', 'http://doi.org/10.1007/978-3-030-39815-6_16', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('polychronou-katsivelis-papakonstantinou-stoitsis-manouselis-machinelearningalgorithmsforfoodintelligencetowardsamethodformoreaccuratepredictions-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{polychronou_machine_2020,\n\ttitle = {Machine {Learning} {Algorithms} for {Food} {Intelligence}: {Towards} a {Method} for {More} {Accurate} {Predictions}},\n\turl = {http://dx.doi.org/10.1007/978-3-030-39815-6_16},\n\tdoi = {10.1007/978-3-030-39815-6_16},\n\tabstract = {It is evident that machine learning algorithms are being widely impacting\nindustrial applications and platforms. Beyond typical research\nexperimentation scenarios, there is a need for companies that wish to\nenhance their online data and analytics solutions to incorporate ways in\nwhich they can select, experiment, benchmark, parameterise and choose the\nversion of a machine learning algorithm that seems to be most appropriate\nfor their specific application context. In this paper, we describe such a\nneed for a big data platform that supports food data analytics and\nintelligence. More specifically, we introduce Agroknow’s big data platform\nand identify the need to extend it with a flexible and interactive\nexperimentation environment where different machine learning algorithms\ncan be tested using a variation of synthetic and real data. A typical\nusage scenario is described, based on our need to experiment with various\nmachine learning algorithms to support price prediction for food products\nand ingredients. The initial requirements for an experimentation\nenvironment are also introduced.},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Polychronou, Ioanna and Katsivelis, Panagis and Papakonstantinou, Mihalis and Stoitsis, Giannis and Manouselis, Nikos},\n\tyear = {2020},\n\tpages = {165--172},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  It is evident that machine learning algorithms are being widely impacting industrial applications and platforms. Beyond typical research experimentation scenarios, there is a need for companies that wish to enhance their online data and analytics solutions to incorporate ways in which they can select, experiment, benchmark, parameterise and choose the version of a machine learning algorithm that seems to be most appropriate for their specific application context. In this paper, we describe such a need for a big data platform that supports food data analytics and intelligence. More specifically, we introduce Agroknow’s big data platform and identify the need to extend it with a flexible and interactive experimentation environment where different machine learning algorithms can be tested using a variation of synthetic and real data. A typical usage scenario is described, based on our need to experiment with various machine learning algorithms to support price prediction for food products and ingredients. The initial requirements for an experimentation environment are also introduced.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1877050919317399\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019', 'http://www.sciencedirect.com/science/article/pii/S1877050919317399', event);\">\n    Personalized Micro-Service Recommendation System for Online News.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asenova, M.;  and Chrysoulas, C.\n</span>\n\n  \n\n</span>\n\n  <i>Procedia Comput. Sci.</i>, 160: 610–615. January 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1877050919317399\"\n     onclick=\"log_download('asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019', 'http://www.sciencedirect.com/science/article/pii/S1877050919317399', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Personalized Micro-Service Recommendation System for Online News [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.procs.2019.11.039\"\n     onclick=\"log_download('asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019', 'http://doi.org/10.1016/j.procs.2019.11.039', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{asenova_personalized_2019,\n\ttitle = {Personalized {Micro}-{Service} {Recommendation} {System} for {Online} {News}},\n\tvolume = {160},\n\tissn = {1877-0509},\n\turl = {http://www.sciencedirect.com/science/article/pii/S1877050919317399},\n\tdoi = {10.1016/j.procs.2019.11.039},\n\tabstract = {In the era of artificial intelligence and high technology advance our life\nis dependent on them in every aspect. The dynamic environment forces us to\nplan our time with conscious and every minute is valuable. To help\nindividuals and corporations see information that is only relevant to\nthem, recommendation systems are in place. Popular platforms that such as\nAmazon, Ebay, Netflix, YouTube, make use of advanced recommendation\nsystems to better serve the needed of their users. This research paper\ngives insight of building a microservice recommendation system for online\nnews. Research in recommendation systems is mainly focused on improving\nuser’s experience based mainly on personalization information, such as\npreferences, and searching history. To determine the initial preferences\nof a user an initial menu of topics/themes is provided for the user to\nchoose from. In order to reflect as precise as possible the searching\ninterests regarding news of user, all of his interactions are thoroughly\nrecorded and in depth analyzed, based on advanced machine learning\ntechniques, when adjusting the news topics, the user is interested for.\nBased on the aforementioned approach, a personalized recommendation system\nfor online news has been developed. Existing techniques has been\nresearched and evaluated to aid the decision about picking the best\napproach for the software to be implemented. Frameworks/technologies used\nfor the development are Java 8, Spring boot, Spring MVC, Maven and\nMongoDB.},\n\tjournal = {Procedia Comput. Sci.},\n\tauthor = {Asenova, Marchela and Chrysoulas, Christos},\n\tmonth = jan,\n\tyear = {2019},\n\tkeywords = {TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases},\n\tpages = {610--615},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In the era of artificial intelligence and high technology advance our life is dependent on them in every aspect. The dynamic environment forces us to plan our time with conscious and every minute is valuable. To help individuals and corporations see information that is only relevant to them, recommendation systems are in place. Popular platforms that such as Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation systems to better serve the needed of their users. This research paper gives insight of building a microservice recommendation system for online news. Research in recommendation systems is mainly focused on improving user’s experience based mainly on personalization information, such as preferences, and searching history. To determine the initial preferences of a user an initial menu of topics/themes is provided for the user to choose from. In order to reflect as precise as possible the searching interests regarding news of user, all of his interactions are thoroughly recorded and in depth analyzed, based on advanced machine learning techniques, when adjusting the news topics, the user is interested for. Based on the aforementioned approach, a personalized recommendation system for online news has been developed. Existing techniques has been researched and evaluated to aid the decision about picking the best approach for the software to be implemented. Frameworks/technologies used for the development are Java 8, Spring boot, Spring MVC, Maven and MongoDB.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019', 'https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf', event);\">\n    Evaluating Recommender System Stability with Influence-Guided Fuzzing.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shriver, D.; Elbaum, S.; Dwyer, M. B;  and Rosenblum, D. S\n</span>\n\n  \n\n</span>\n\n  In  2019. AAAI\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf\"\n     onclick=\"log_download('shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019', 'https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Evaluating Recommender System Stability with Influence-Guided Fuzzing [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shriver-elbaum-dwyer-rosenblum-evaluatingrecommendersystemstabilitywithinfluenceguidedfuzzing-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{shriver_evaluating_2019,\n\ttitle = {Evaluating {Recommender} {System} {Stability} with {Influence}-{Guided} {Fuzzing}},\n\turl = {https://www.comp.nus.edu.sg/~david/Publications/aaai2019-preprint.pdf},\n\tabstract = {Recommender systems help users to find products or services they may like\nwhen lacking personal experience or facing an overwhelming set of choices.\nSince unstable recommendations can lead to distrust, loss of profits, and\na poor user experience, it is important to test recommender system\nstability. In this work, we present an approach based on inferred models\nof influence that underlie recommender systems to guide the generation of\ndataset modifications to assess a recommender&#x27;s stability. We implement\nour approach …},\n\tpublisher = {AAAI},\n\tauthor = {Shriver, David and Elbaum, Sebastian and Dwyer, Matthew B and Rosenblum, David S},\n\tyear = {2019},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender's stability. We implement our approach …\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019', 'https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf', event);\">\n    Things you might not know about the k-Nearest neighbors algorithm.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karpus, A.; Raczyńska, M;  and Przybyłek, A\n</span>\n\n  \n\n</span>\n\n  In  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf\"\n     onclick=\"log_download('karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019', 'https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Things you might not know about the k-Nearest neighbors algorithm [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karpus-raczyska-przybyek-thingsyoumightnotknowabouttheknearestneighborsalgorithm-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{karpus_things_2019,\n\ttitle = {Things you might not know about the k-{Nearest} neighbors algorithm},\n\turl = {https://www.researchgate.net/profile/Adam_Przybylek/publication/336235570_Things_You_Might_Not_Know_about_the_k-Nearest_Neighbors_Algorithm/links/5daf2307a6fdccc99d92bf9f/Things-You-Might-Not-Know-about-the-k-Nearest-Neighbors-Algorithm.pdf},\n\tauthor = {Karpus, Aleksandra and Raczyńska, M and Przybyłek, A},\n\tyear = {2019},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (10)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://proceedings.mlr.press/v81/ekstrand18b.html\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018', 'https://proceedings.mlr.press/v81/ekstrand18b.html', event);\">\n    All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D; Tian, M.; Azpiazu, I. M.; Ekstrand, J. D; Anuyah, O.; McNeill, D.;  and Pera, M. S.\n</span>\n\n  \n\n</span>\n\n  In Friedler, S. A;  and Wilson, C., editor(s), <i>Proceedings of the 1st Conference on Fairness, Accountability and Transparency</i>, volume 81, of <i>Proceedings of Machine Learning Research</i>, pages 172–186,  2018. PMLR\n  <span class=\"note\">Journal Abbreviation: Proceedings of Machine Learning Research</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://proceedings.mlr.press/v81/ekstrand18b.html\"\n     onclick=\"log_download('ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018', 'https://proceedings.mlr.press/v81/ekstrand18b.html', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-tian-azpiazu-ekstrand-anuyah-mcneill-pera-allthecoolkidshowdotheyfitinpopularityanddemographicbiasesinrecommenderevaluationandeffectiveness-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ekstrand_all_2018,\n\tseries = {Proceedings of {Machine} {Learning} {Research}},\n\ttitle = {All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness},\n\tvolume = {81},\n\turl = {https://proceedings.mlr.press/v81/ekstrand18b.html},\n\tabstract = {In the research literature, evaluations of recommender system\neffectiveness typically report results over a given data set, providing an\naggregate measure of effectiveness over each instance (e.g. user) in the\ndata set. Recent advances in information retrieval evaluation, however,\ndemonstrate the importance of considering the distribution of\neffectiveness across diverse groups of varying sizes. For example, do\nusers of different ages or genders obtain similar utility from the system,\nparticularly if their group is a relatively small subset of the user base?\nWe apply this consideration to recommender systems, using offline\nevaluation and a utility-based metric of recommendation effectiveness to\nexplore whether different user demographic groups experience similar\nrecommendation accuracy. We find demographic differences in measured\nrecommender effectiveness across two data sets containing different types\nof feedback in different domains; these differences sometimes, but not\nalways, correlate with the size of the user group in question. Demographic\neffects also have a complex—and likely detrimental—interaction with\npopularity bias, a known deficiency of recommender evaluation. These\nresults demonstrate the need for recommender system evaluation protocols\nthat explicitly quantify the degree to which the system is meeting the\ninformation needs of all its users, as well as the need for researchers\nand operators to move beyond naïve evaluations that favor the needs of\nlarger subsets of the user population while ignoring smaller subsets.},\n\tbooktitle = {Proceedings of the 1st {Conference} on {Fairness}, {Accountability} and {Transparency}},\n\tpublisher = {PMLR},\n\tauthor = {Ekstrand, Michael D and Tian, Mucun and Azpiazu, Ion Madrazo and Ekstrand, Jennifer D and Anuyah, Oghenemaro and McNeill, David and Pera, Maria Soledad},\n\teditor = {Friedler, Sorelle A and Wilson, Christo},\n\tyear = {2018},\n\tnote = {Journal Abbreviation: Proceedings of Machine Learning Research},\n\tpages = {172--186},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In the research literature, evaluations of recommender system effectiveness typically report results over a given data set, providing an aggregate measure of effectiveness over each instance (e.g. user) in the data set. Recent advances in information retrieval evaluation, however, demonstrate the importance of considering the distribution of effectiveness across diverse groups of varying sizes. For example, do users of different ages or genders obtain similar utility from the system, particularly if their group is a relatively small subset of the user base? We apply this consideration to recommender systems, using offline evaluation and a utility-based metric of recommendation effectiveness to explore whether different user demographic groups experience similar recommendation accuracy. We find demographic differences in measured recommender effectiveness across two data sets containing different types of feedback in different domains; these differences sometimes, but not always, correlate with the size of the user group in question. Demographic effects also have a complex—and likely detrimental—interaction with popularity bias, a known deficiency of recommender evaluation. These results demonstrate the need for recommender system evaluation protocols that explicitly quantify the degree to which the system is meeting the information needs of all its users, as well as the need for researchers and operators to move beyond naïve evaluations that favor the needs of larger subsets of the user population while ignoring smaller subsets.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3240323.3240373\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018', 'https://dl.acm.org/doi/10.1145/3240323.3240373', event);\">\n    Exploring author gender in book rating and recommendation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D; Tian, M.; Kazi, M. R I.; Mehrpouyan, H.;  and Kluver, D.\n</span>\n\n  \n\n</span>\n\n  In New York, NY, USA, September 2018. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3240323.3240373\"\n     onclick=\"log_download('ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018', 'https://dl.acm.org/doi/10.1145/3240323.3240373', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Exploring author gender in book rating and recommendation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3240323.3240373\"\n     onclick=\"log_download('ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018', 'http://doi.org/10.1145/3240323.3240373', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-tian-kazi-mehrpouyan-kluver-exploringauthorgenderinbookratingandrecommendation-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ekstrand_exploring_2018,\n\taddress = {New York, NY, USA},\n\ttitle = {Exploring author gender in book rating and recommendation},\n\turl = {https://dl.acm.org/doi/10.1145/3240323.3240373},\n\tdoi = {10.1145/3240323.3240373},\n\tabstract = {Collaborative filtering algorithms find useful patterns in rating and\nconsumption data and exploit these patterns to guide users to good items.\nMany of the patterns in rating datasets reflect important real-world\ndifferences between the various users and items in the data; other\npatterns may be irrelevant or possibly undesirable for social or ethical\nreasons, particularly if they reflect undesired discrimination, such as\ngender or ethnic discrimination in publishing. In this work, we examine\nthe response of collaborative filtering recommender algorithms to the\ndistribution of their input data with respect to a dimension of social\nconcern, namely content creator gender. Using publicly-available book\nratings data, we measure the distribution of the genders of the authors of\nbooks in user rating profiles and recommendation lists produced from this\ndata. We find that common collaborative filtering algorithms differ in the\ngender distribution of their recommendation lists, and in the relationship\nof that output distribution to user profile distribution.},\n\tpublisher = {ACM},\n\tauthor = {Ekstrand, Michael D and Tian, Mucun and Kazi, Mohammed R Imran and Mehrpouyan, Hoda and Kluver, Daniel},\n\tmonth = sep,\n\tyear = {2018},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as gender or ethnic discrimination in publishing. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-2225/paper6.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018', 'http://ceur-ws.org/Vol-2225/paper6.pdf', event);\">\n    From recommendation to curation: when the system becomes your personal docent.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dragovic, N.; Azpiazu, I. M.;  and Pera, M. S.\n</span>\n\n  \n\n</span>\n\n  In pages 37–44, October 2018. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-2225/paper6.pdf\"\n     onclick=\"log_download('dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018', 'http://ceur-ws.org/Vol-2225/paper6.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"From recommendation to curation: when the system becomes your personal docent [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dragovic-azpiazu-pera-fromrecommendationtocurationwhenthesystembecomesyourpersonaldocent-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{dragovic_recommendation_2018,\n\ttitle = {From recommendation to curation: when the system becomes your personal docent},\n\turl = {http://ceur-ws.org/Vol-2225/paper6.pdf},\n\tabstract = {Curation is the act of selecting, organizing, and presenting content. Some\napplications emulate this process by turning users into curators, while\nothers use recommenders to select items, seldom achieving the focus or\nselectivity of human curators. We bridge this gap with a …},\n\tauthor = {Dragovic, Nevena and Azpiazu, Ion Madrazo and Pera, Maria Soledad},\n\tmonth = oct,\n\tyear = {2018},\n\tpages = {37--44},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a …\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0950705118304799\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018', 'http://www.sciencedirect.com/science/article/pii/S0950705118304799', event);\">\n    User preferences modeling using dirichlet process mixture model for a content-based recommender system.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cami, B. R.; Hassanpour, H.;  and Mashayekhi, H.\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge-Based Systems</i>. September 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0950705118304799\"\n     onclick=\"log_download('cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018', 'http://www.sciencedirect.com/science/article/pii/S0950705118304799', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"User preferences modeling using dirichlet process mixture model for a content-based recommender system [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.knosys.2018.09.028\"\n     onclick=\"log_download('cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018', 'http://doi.org/10.1016/j.knosys.2018.09.028', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{cami_user_2018,\n\ttitle = {User preferences modeling using dirichlet process mixture model for a content-based recommender system},\n\tissn = {0950-7051},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0950705118304799},\n\tdoi = {10.1016/j.knosys.2018.09.028},\n\tabstract = {Recommender systems have been developed to assist users in retrieving\nrelevant resources. Collaborative and content-based filtering are two\nbasic approaches that are used in recommender systems. The former employs\nthe feedback of users with similar interests, while the latter is based on\nthe feature of the selected resources by each user. Recommender systems\ncan consider users’ behavior to more accurately estimate their preferences\nvia a list of recommendations. However, the existing approaches rarely\nconsider both interests and preferences of the users. Also, the dynamic\nnature of user behavior poses an additional challenge for recommender\nsystems. In this paper, we consider the interactions of each individual\nuser, and analyze them to propose a user model and capture user’s\ninterests. We construct the user model based on a Bayesian nonparametric\nframework, called the Dirichlet Process Mixture Model. The proposed model\nevolves following the dynamic nature of user behavior to adapt both the\nuser interests and preferences. We implemented the proposed model and\nevaluated it using both the MovieLens dataset, and a real-world dataset\nthat contains news tweets from five news channels (New York Times, BBC,\nCNN, Reuters and Associated Press). The experimental results and\ncomparisons with several recently developed approaches show the\nsuperiority in accuracy of the proposed approach, and its ability to adapt\nwith user behavior over time.},\n\tjournal = {Knowledge-Based Systems},\n\tauthor = {Cami, Bagher Rahimpour and Hassanpour, Hamid and Mashayekhi, Hoda},\n\tmonth = sep,\n\tyear = {2018},\n\tkeywords = {Temporal content-based recommender systems, User behavior modeling, User preferences modeling},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems have been developed to assist users in retrieving relevant resources. Collaborative and content-based filtering are two basic approaches that are used in recommender systems. The former employs the feedback of users with similar interests, while the latter is based on the feature of the selected resources by each user. Recommender systems can consider users’ behavior to more accurately estimate their preferences via a list of recommendations. However, the existing approaches rarely consider both interests and preferences of the users. Also, the dynamic nature of user behavior poses an additional challenge for recommender systems. In this paper, we consider the interactions of each individual user, and analyze them to propose a user model and capture user’s interests. We construct the user model based on a Bayesian nonparametric framework, called the Dirichlet Process Mixture Model. The proposed model evolves following the dynamic nature of user behavior to adapt both the user interests and preferences. We implemented the proposed model and evaluated it using both the MovieLens dataset, and a real-world dataset that contains news tweets from five news channels (New York Times, BBC, CNN, Reuters and Associated Press). The experimental results and comparisons with several recently developed approaches show the superiority in accuracy of the proposed approach, and its ability to adapt with user behavior over time.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1007/978-3-319-95168-3_26\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018', 'http://dx.doi.org/10.1007/978-3-319-95168-3_26', event);\">\n    FAiR: A Framework for Analyses and Evaluations on Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Carvalho, D.; Silva, N.; Silveira, T.; Mourão, F.; Pereira, A.; Dias, D.;  and Rocha, L.\n</span>\n\n  \n\n</span>\n\n  In pages 383–397,  2018. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1007/978-3-319-95168-3_26\"\n     onclick=\"log_download('carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018', 'http://dx.doi.org/10.1007/978-3-319-95168-3_26', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"FAiR: A Framework for Analyses and Evaluations on Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-319-95168-3_26\"\n     onclick=\"log_download('carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018', 'http://doi.org/10.1007/978-3-319-95168-3_26', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('carvalho-silva-silveira-mouro-pereira-dias-rocha-fairaframeworkforanalysesandevaluationsonrecommendersystems-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{carvalho_fair_2018,\n\ttitle = {{FAiR}: {A} {Framework} for {Analyses} and {Evaluations} on {Recommender} {Systems}},\n\turl = {http://dx.doi.org/10.1007/978-3-319-95168-3_26},\n\tdoi = {10.1007/978-3-319-95168-3_26},\n\tabstract = {Recommender systems (RSs) have become essential tools in e-commerce\napplications, helping users in the decision-making process. Evaluation on\nthese tools is, however, a major divergence point nowadays, since there is\nno consensus regarding which metrics are necessary to consolidate new RSs.\nFor this reason, distinct frameworks have been developed to ease the\ndeployment of RSs in research and/or production environments. In the\npresent work, we perform an extensive study of the most popular evaluation\nmetrics, organizing them into three groups: Effectiveness-based,\nComplementary Dimensions of Quality and Domain Profiling. Further, we\nconsolidate a framework named FAiR to help researchers in evaluating their\nRSs using these metrics, besides identifying the characteristics of data\ncollections that may intrinsically affect RSs performance. FAiR is\ncompatible with the output format of the main existing RSs libraries\n(i.e., MyMediaLite and LensKit).},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Carvalho, Diego and Silva, Nícollas and Silveira, Thiago and Mourão, Fernando and Pereira, Adriano and Dias, Diego and Rocha, Leonardo},\n\tyear = {2018},\n\tpages = {383--397},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems (RSs) have become essential tools in e-commerce applications, helping users in the decision-making process. Evaluation on these tools is, however, a major divergence point nowadays, since there is no consensus regarding which metrics are necessary to consolidate new RSs. For this reason, distinct frameworks have been developed to ease the deployment of RSs in research and/or production environments. In the present work, we perform an extensive study of the most popular evaluation metrics, organizing them into three groups: Effectiveness-based, Complementary Dimensions of Quality and Domain Profiling. Further, we consolidate a framework named FAiR to help researchers in evaluating their RSs using these metrics, besides identifying the characteristics of data collections that may intrinsically affect RSs performance. FAiR is compatible with the output format of the main existing RSs libraries (i.e., MyMediaLite and LensKit).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/3227609.3227671\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018', 'http://doi.acm.org/10.1145/3227609.3227671', event);\">\n    Replicating and Improving Top-N Recommendations in Open Source Packages.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Coba, L.; Symeonidis, P.;  and Zanker, M.\n</span>\n\n  \n\n</span>\n\n  In <i>WIMS '18</i>, pages 40:1–40:7, New York, NY, USA,  2018. ACM\n  <span class=\"note\">Journal Abbreviation: WIMS '18</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/3227609.3227671\"\n     onclick=\"log_download('coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018', 'http://doi.acm.org/10.1145/3227609.3227671', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Replicating and Improving Top-N Recommendations in Open Source Packages [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3227609.3227671\"\n     onclick=\"log_download('coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018', 'http://doi.org/10.1145/3227609.3227671', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('coba-symeonidis-zanker-replicatingandimprovingtopnrecommendationsinopensourcepackages-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{coba_replicating_2018,\n\taddress = {New York, NY, USA},\n\ttitle = {Replicating and {Improving} {Top}-{N} {Recommendations} in {Open} {Source} {Packages}},\n\turl = {http://doi.acm.org/10.1145/3227609.3227671},\n\tdoi = {10.1145/3227609.3227671},\n\tbooktitle = {{WIMS} &#x27;18},\n\tpublisher = {ACM},\n\tauthor = {Coba, Ludovik and Symeonidis, Panagiotis and Zanker, Markus},\n\tyear = {2018},\n\tnote = {Journal Abbreviation: WIMS &#x27;18},\n\tkeywords = {Collaborative Filtering, Recommendation algorithms, evaluation},\n\tpages = {40:1--40:7},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1109/TMM.2017.2779043\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018', 'http://dx.doi.org/10.1109/TMM.2017.2779043', event);\">\n    Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yang, Y; Xu, Y; Wang, E; Han, J;  and Yu, Z\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Trans. Multimedia</i>, 20(7): 1888–1900. July 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1109/TMM.2017.2779043\"\n     onclick=\"log_download('yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018', 'http://dx.doi.org/10.1109/TMM.2017.2779043', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/TMM.2017.2779043\"\n     onclick=\"log_download('yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018', 'http://doi.org/10.1109/TMM.2017.2779043', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yang-xu-wang-han-yu-improvingexistingcollaborativefilteringrecommendationsviaserendipitybasedalgorithm-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yang_improving_2018,\n\ttitle = {Improving {Existing} {Collaborative} {Filtering} {Recommendations} via {Serendipity}-{Based} {Algorithm}},\n\tvolume = {20},\n\tissn = {1520-9210},\n\turl = {http://dx.doi.org/10.1109/TMM.2017.2779043},\n\tdoi = {10.1109/TMM.2017.2779043},\n\tabstract = {In this paper, we study how to address the sparsity, accuracy and\nserendipity issues of top-N recommendation with collaborative filtering\n(CF). Existing studies commonly use rated items (which form only a small\nsection in a rating matrix) or import some additional information (e.g.,\ndetails about the items and users) to improve the performance of CF.\nUnlike these methods, we propose a novel notion towards a huge amount of\nunrated items: serendipity item. By utilizing serendipity items, we\npropose concise satisfaction and interest injection (CSII), a method that\ncan effectively find interesting, satisfying, and serendipitous items in\nunrated items. By preventing uninteresting and unsatisfying items to be\nrecommended as top-N items, this concise-but-novel method improves\naccuracy and recommendation quality (especially serendipity)\nsubstantially. Meanwhile, it can address the sparsity and cold-start\nissues by enriching the rating matrix in CF without additional\ninformation. As our method tackles rating matrix before recommendation\nprocedure, it can be applied to most existing CF methods, such as\nitem-based CF, user-based CF and matrix factorization-based CF. Through\ncomprehensive experiments using abundant real-world datasets with LensKit\nimplementation, we successfully demonstrate that our solution improves the\nperformance of existing CF methods consistently and universally. Moreover,\ncomparing with baseline methods, CSII can extract uninteresting items more\ncarefully and cautiously, avoiding potential items inferred by mistake.},\n\tnumber = {7},\n\tjournal = {IEEE Trans. Multimedia},\n\tauthor = {Yang, Y and Xu, Y and Wang, E and Han, J and Yu, Z},\n\tmonth = jul,\n\tyear = {2018},\n\tkeywords = {CF methods, CSII, Collaboration, Collaborative filtering, Computer science, Data mining, Lifting equipment, Multimedia communication, Recommender systems, cold-start issues, collaborative filtering, collaborative filtering recommendations, concise satisfaction and interest injection, item-based CF, matrix decomposition, matrix factorization, matrix factorization-based CF, rating matrix, recommendation quality, recommender systems, serendipitous recommendation, serendipity item, top-N items, top-N recommendation, unrated items, user-based CF},\n\tpages = {1888--1900},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N recommendation with collaborative filtering (CF). Existing studies commonly use rated items (which form only a small section in a rating matrix) or import some additional information (e.g., details about the items and users) to improve the performance of CF. Unlike these methods, we propose a novel notion towards a huge amount of unrated items: serendipity item. By utilizing serendipity items, we propose concise satisfaction and interest injection (CSII), a method that can effectively find interesting, satisfying, and serendipitous items in unrated items. By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially. Meanwhile, it can address the sparsity and cold-start issues by enriching the rating matrix in CF without additional information. As our method tackles rating matrix before recommendation procedure, it can be applied to most existing CF methods, such as item-based CF, user-based CF and matrix factorization-based CF. Through comprehensive experiments using abundant real-world datasets with LensKit implementation, we successfully demonstrate that our solution improves the performance of existing CF methods consistently and universally. Moreover, comparing with baseline methods, CSII can extract uninteresting items more carefully and cautiously, avoiding potential items inferred by mistake.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shriver-assessingthequalityandstabilityofrecommendersystems-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://digitalcommons.unl.edu/computerscidiss/147\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shriver-assessingthequalityandstabilityofrecommendersystems-2018', 'https://digitalcommons.unl.edu/computerscidiss/147', event);\">\n    Assessing the Quality and Stability of Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shriver, D.\n</span>\n\n  \n\n</span>\n\n  Master's thesis, University of Nebraska - Lincoln,  2018.\n  <span class=\"note\">Publication Title: Computer Science and Engineering</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://digitalcommons.unl.edu/computerscidiss/147\"\n     onclick=\"log_download('shriver-assessingthequalityandstabilityofrecommendersystems-2018', 'https://digitalcommons.unl.edu/computerscidiss/147', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Assessing the Quality and Stability of Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shriver-assessingthequalityandstabilityofrecommendersystems-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shriver-assessingthequalityandstabilityofrecommendersystems-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@mastersthesis{shriver_assessing_2018,\n\ttitle = {Assessing the {Quality} and {Stability} of {Recommender} {Systems}},\n\turl = {https://digitalcommons.unl.edu/computerscidiss/147},\n\tabstract = {Recommender systems help users to find products they may like when lacking\npersonal experience or facing an overwhelmingly large set of items.\nHowever, assessing the quality and stability of recommender systems can\npresent challenges for developers. First, traditional accuracy metrics,\nsuch as precision and recall, for validating the quality of\nrecommendations, offer only a coarse, one-dimensional view of the system\nperformance. Second, assessing the stability of a recommender systems\nrequires generating new data and retraining a system, which is expensive.\nIn this work, we present two new approaches for assessing the quality and\nstability of recommender systems to address these challenges. We first\npresent a general and extensible approach for assessing the quality of the\nbehavior of a recommender system using logical property templates. The\napproach is general in that it defines recommendation systems in terms of\nsets of rankings, ratings, users, and items on which property templates\nare defined. It is extensible in that these property templates define a\nspace of properties that can be instantiated and parameterized to\ncharacterize a recommendation system. We study the application of the\napproach to several recommendation systems. Our findings demonstrate the\npotential of these properties, illustrating the insights they can provide\nabout the different algorithms and evolving datasets. We also present an\napproach for influence-guided fuzz testing of recommender system\nstability. We infer influence models for aspects of a dataset, such as\nusers or items, from the recommendations produced by a recommender system\nand its training data. We define dataset fuzzing heuristics that use these\ninfluence models for generating modifications to an original dataset and\nwe present a test oracle based on a threshold of acceptable instability.\nWe implement our approach and evaluate it on several recommender\nalgorithms using the MovieLens dataset and we find that influence-guided\nfuzzing can effectively find small sets of modifications that cause\nsignificantly more instability than random approaches. Adviser: Sebastian\nElbaum},\n\turldate = {2018-05-08},\n\tschool = {University of Nebraska - Lincoln},\n\tauthor = {Shriver, David},\n\tcollaborator = {Elbaum, Sebastian},\n\tyear = {2018},\n\tnote = {Publication Title: Computer Science and Engineering},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems help users to find products they may like when lacking personal experience or facing an overwhelmingly large set of items. However, assessing the quality and stability of recommender systems can present challenges for developers. First, traditional accuracy metrics, such as precision and recall, for validating the quality of recommendations, offer only a coarse, one-dimensional view of the system performance. Second, assessing the stability of a recommender systems requires generating new data and retraining a system, which is expensive. In this work, we present two new approaches for assessing the quality and stability of recommender systems to address these challenges. We first present a general and extensible approach for assessing the quality of the behavior of a recommender system using logical property templates. The approach is general in that it defines recommendation systems in terms of sets of rankings, ratings, users, and items on which property templates are defined. It is extensible in that these property templates define a space of properties that can be instantiated and parameterized to characterize a recommendation system. We study the application of the approach to several recommendation systems. Our findings demonstrate the potential of these properties, illustrating the insights they can provide about the different algorithms and evolving datasets. We also present an approach for influence-guided fuzz testing of recommender system stability. We infer influence models for aspects of a dataset, such as users or items, from the recommendations produced by a recommender system and its training data. We define dataset fuzzing heuristics that use these influence models for generating modifications to an original dataset and we present a test oracle based on a threshold of acceptable instability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset and we find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches. Adviser: Sebastian Elbaum\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kotkov-serendipityinrecommendersystems-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://jyx.jyu.fi/handle/123456789/58207\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kotkov-serendipityinrecommendersystems-2018', 'https://jyx.jyu.fi/handle/123456789/58207', event);\">\n    Serendipity in recommender systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kotkov, D.\n</span>\n\n  \n\n</span>\n\n  University of Jyväskylä,  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://jyx.jyu.fi/handle/123456789/58207\"\n     onclick=\"log_download('kotkov-serendipityinrecommendersystems-2018', 'https://jyx.jyu.fi/handle/123456789/58207', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Serendipity in recommender systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kotkov-serendipityinrecommendersystems-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kotkov-serendipityinrecommendersystems-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@book{kotkov_serendipity_2018,\n\ttitle = {Serendipity in recommender systems},\n\tisbn = {978-951-39-7438-1},\n\turl = {https://jyx.jyu.fi/handle/123456789/58207},\n\tabstract = {The number of goods and services (such as accommodation or music\nstreaming) offered by e-commerce websites does not allow users to examine\nall the available options in a reasonable amount of time. Recommender\nsystems are auxiliary systems designed to help users find interesting goods\nor services (items) on a website when the number of available items is\noverwhelming. Traditionally, recommender systems have been optimized for\naccuracy, which indicates how often a user consumed the items recommended\nby system. To increase accuracy, recommender systems often suggest items\nthat are popular and suitably similar to items these users have consumed\nin the past. As a result, users often lose interest in using these\nsystems, as they either know about the recommended items already or can\neasily find these items themselves. One way to increase user satisfaction\nand user retention is to suggest serendipitous items. These items are\nitems that users would not find themselves or even look for, but would\nenjoy consuming. Serendipity in recommender systems has not been\nthoroughly investigated. There is not even a consensus on the concept’s\ndefinition. In this dissertation, serendipitous items are defined as\nrelevant, novel and unexpected to a user. In this dissertation, we (a)\nreview different definitions of the concept and evaluate them in a user\nstudy, (b) assess the proportion of serendipitous items in a typical\nrecommender system, (c) review ways to measure and improve serendipity,\n(d) investigate serendipity in cross-domain recommender systems (systems\nthat take advantage of multiple domains, such as movies, songs and books)\nand (e) discuss challenges and future directions concerning this topic. We\napplied a Design Science methodology as the framework for this study and\ndeveloped four artifacts: (1) a collection of eight variations of\nserendipity definition, (2) a measure of the serendipity of suggested\nitems, (3) an algorithm that generates serendipitous suggestions, (4) a\ndataset of user feedback regarding serendipitous movies in the recommender\nsystem MovieLens. These artifacts are evaluated using suitable methods and\ncommunicated through publications.},\n\turldate = {2018-07-06},\n\tpublisher = {University of Jyväskylä},\n\tauthor = {Kotkov, Denis},\n\tyear = {2018},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are auxiliary systems designed to help users find interesting goods or services (items) on a website when the number of available items is overwhelming. Traditionally, recommender systems have been optimized for accuracy, which indicates how often a user consumed the items recommended by system. To increase accuracy, recommender systems often suggest items that are popular and suitably similar to items these users have consumed in the past. As a result, users often lose interest in using these systems, as they either know about the recommended items already or can easily find these items themselves. One way to increase user satisfaction and user retention is to suggest serendipitous items. These items are items that users would not find themselves or even look for, but would enjoy consuming. Serendipity in recommender systems has not been thoroughly investigated. There is not even a consensus on the concept’s definition. In this dissertation, serendipitous items are defined as relevant, novel and unexpected to a user. In this dissertation, we (a) review different definitions of the concept and evaluate them in a user study, (b) assess the proportion of serendipitous items in a typical recommender system, (c) review ways to measure and improve serendipity, (d) investigate serendipity in cross-domain recommender systems (systems that take advantage of multiple domains, such as movies, songs and books) and (e) discuss challenges and future directions concerning this topic. We applied a Design Science methodology as the framework for this study and developed four artifacts: (1) a collection of eight variations of serendipity definition, (2) a measure of the serendipity of suggested items, (3) an algorithm that generates serendipitous suggestions, (4) a dataset of user feedback regarding serendipitous movies in the recommender system MovieLens. These artifacts are evaluated using suitable methods and communicated through publications.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/article/10.1007/s11042-018-5640-2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018', 'https://link.springer.com/article/10.1007/s11042-018-5640-2', event);\">\n    Heart rate monitoring, activity recognition, and recommendation for e-coaching.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  De Pessemier, T.;  and Martens, L.\n</span>\n\n  \n\n</span>\n\n  <i>Multimed. Tools Appl.</i>,1–18. January 2018.\n  <span class=\"note\">Publisher: Springer US</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/article/10.1007/s11042-018-5640-2\"\n     onclick=\"log_download('depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018', 'https://link.springer.com/article/10.1007/s11042-018-5640-2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Heart rate monitoring, activity recognition, and recommendation for e-coaching [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11042-018-5640-2\"\n     onclick=\"log_download('depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018', 'http://doi.org/10.1007/s11042-018-5640-2', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('depessemier-martens-heartratemonitoringactivityrecognitionandrecommendationforecoaching-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{de_pessemier_heart_2018,\n\ttitle = {Heart rate monitoring, activity recognition, and recommendation for e-coaching},\n\tissn = {1380-7501},\n\turl = {https://link.springer.com/article/10.1007/s11042-018-5640-2},\n\tdoi = {10.1007/s11042-018-5640-2},\n\tabstract = {Equipped with hardware, such as accelerometer and heart rate sensor,\nwearables enable measuring physical activities and heart rate. However,\nthe accuracy of these heart rate measurements is still unclear and the\ncoupling with activity recognition is often missing in health apps. This\nstudy evaluates heart rate monitoring with four different device types: a\nspecialized sports device with chest strap, a fitness tracker, a smart\nwatch, and a smartphone using photoplethysmography. In a state of rest,\nsimilar measurement results are obtained with the four devices. During\nphysical activities, the fitness tracker, smart watch, and smartphone\nmeasure sudden variations in heart rate with a delay, due to movements of\nthe wrist. Moreover, this study showed that physical activities, such as\nsquats and dumbbell curl, can be recognized with fitness trackers. By\ncombining heart rate monitoring and activity recognition, personal\nsuggestions for physical activities are generated using a tag-based\nrecommender and rule-based filter.},\n\turldate = {2018-02-08},\n\tjournal = {Multimed. Tools Appl.},\n\tauthor = {De Pessemier, Toon and Martens, Luc},\n\tmonth = jan,\n\tyear = {2018},\n\tnote = {Publisher: Springer US},\n\tpages = {1--18},\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Equipped with hardware, such as accelerometer and heart rate sensor, wearables enable measuring physical activities and heart rate. However, the accuracy of these heart rate measurements is still unclear and the coupling with activity recognition is often missing in health apps. This study evaluates heart rate monitoring with four different device types: a specialized sports device with chest strap, a fitness tracker, a smart watch, and a smartphone using photoplethysmography. In a state of rest, similar measurement results are obtained with the four devices. During physical activities, the fitness tracker, smart watch, and smartphone measure sudden variations in heart rate with a delay, due to movements of the wrist. Moreover, this study showed that physical activities, such as squats and dumbbell curl, can be recognized with fitness trackers. By combining heart rate monitoring and activity recognition, personal suggestions for physical activities are generated using a tag-based recommender and rule-based filter.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (6)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://aaai.org/papers/639-flairs-2017-15534/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017', 'https://aaai.org/papers/639-flairs-2017-15534/', event);\">\n    Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D;  and Mahant, V.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 30th Florida Artificial Intelligence Research Society Conference</i>, of <i>FLAIRS 30</i>, May 2017. AAAI Press\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://aaai.org/papers/639-flairs-2017-15534/\"\n     onclick=\"log_download('ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017', 'https://aaai.org/papers/639-flairs-2017-15534/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-mahant-sturgeonandthecoolkidsproblemswithtopnrecommenderevaluation-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ekstrand_sturgeon_2017,\n\tseries = {{FLAIRS} 30},\n\ttitle = {Sturgeon and the {Cool} {Kids}: {Problems} with {Top}-{N} {Recommender} {Evaluation}},\n\turl = {https://aaai.org/papers/639-flairs-2017-15534/},\n\tabstract = {Top-N evaluation of recommender systems, typically carried out using\nmetrics from information retrieval or machine learning, has several\nchallenges. Two of these challenges are popularity bias, where the\nevaluation intrinsically favors algorithms that recommend popular items,\nand misclassified decoys, where items for which no user relevance is known\nare actually relevant to the user, but the evaluation is unaware and\npenalizes the recommender for suggesting them. One strategy for mitigating\nthe misclassified decoy problem is the one-plus-random evaluation strategy\nand its generalization, which we call random decoys. In this work, we\nexplore the random decoy strategy through both a theoretical treatment and\nan empirical study, but find little evidence to guide its tuning and show\nthat it has complex and deleterious interactions with popularity bias.},\n\tbooktitle = {Proceedings of the 30th {Florida} {Artificial} {Intelligence} {Research} {Society} {Conference}},\n\tpublisher = {AAAI Press},\n\tauthor = {Ekstrand, Michael D and Mahant, Vaibhav},\n\tmonth = may,\n\tyear = {2017},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Top-N evaluation of recommender systems, typically carried out using metrics from information retrieval or machine learning, has several challenges. Two of these challenges are popularity bias, where the evaluation intrinsically favors algorithms that recommend popular items, and misclassified decoys, where items for which no user relevance is known are actually relevant to the user, but the evaluation is unaware and penalizes the recommender for suggesting them. One strategy for mitigating the misclassified decoy problem is the one-plus-random evaluation strategy and its generalization, which we call random decoys. In this work, we explore the random decoy strategy through both a theoretical treatment and an empirical study, but find little evidence to guide its tuning and show that it has complex and deleterious interactions with popularity bias.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://aaai.org/papers/657-flairs-2017-15524/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017', 'https://aaai.org/papers/657-flairs-2017-15524/', event);\">\n    Recommender response to diversity and popularity bias in user profiles.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Channamsetty, S.;  and Ekstrand, M. D\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 30th Florida artificial intelligence research society conference</i>, May 2017. AAAI Press\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://aaai.org/papers/657-flairs-2017-15524/\"\n     onclick=\"log_download('channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017', 'https://aaai.org/papers/657-flairs-2017-15524/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommender response to diversity and popularity bias in user profiles [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('channamsetty-ekstrand-recommenderresponsetodiversityandpopularitybiasinuserprofiles-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{channamsetty_recommender_2017,\n\ttitle = {Recommender response to diversity and popularity bias in user profiles},\n\turl = {https://aaai.org/papers/657-flairs-2017-15524/},\n\tabstract = {Recommender system evaluation usually focuses on the overall effectiveness\nof the algorithms, either in terms of measurable accuracy or ability to\ndeliver user satisfaction or improve business metrics. When additional\nfactors are considered, such as the diversity or novelty of the\nrecommendations, the focus typically remains on the algorithm’s overall\nperformance. We examine the relationship of the recommender’s output\ncharacteristics – accuracy, popularity (as an inverse of novelty), and\ndiversity – to characteristics of the user’s rating profile. The aims of\nthis analysis are twofold: (1) to probe the conditions under which common\nalgorithms produce more or less diverse or popular recommendations, and\n(2) to determine if these personalized recommender algorithms reflect a\nuser’s preference for diversity or novelty. We trained recommenders on the\nMovieLens data and looked for correlation between the user profile and the\nrecommender’s output for both diversity and popularity bias using\ndifferent metrics. We find that the diversity and popularity of movies in\nusers’ profiles has little impact on the recommendations they receive.},\n\turldate = {2017-05-29},\n\tbooktitle = {Proceedings of the 30th {Florida} artificial intelligence research society conference},\n\tpublisher = {AAAI Press},\n\tauthor = {Channamsetty, Sushma and Ekstrand, Michael D},\n\tmonth = may,\n\tyear = {2017},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender system evaluation usually focuses on the overall effectiveness of the algorithms, either in terms of measurable accuracy or ability to deliver user satisfaction or improve business metrics. When additional factors are considered, such as the diversity or novelty of the recommendations, the focus typically remains on the algorithm’s overall performance. We examine the relationship of the recommender’s output characteristics – accuracy, popularity (as an inverse of novelty), and diversity – to characteristics of the user’s rating profile. The aims of this analysis are twofold: (1) to probe the conditions under which common algorithms produce more or less diverse or popular recommendations, and (2) to determine if these personalized recommender algorithms reflect a user’s preference for diversity or novelty. We trained recommenders on the MovieLens data and looked for correlation between the user profile and the recommender’s output for both diversity and popularity bias using different metrics. We find that the diversity and popularity of movies in users’ profiles has little impact on the recommendations they receive.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1109/BigDataService.2017.28\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017', 'http://dx.doi.org/10.1109/BigDataService.2017.28', event);\">\n    Scaling Collaborative Filtering to Large-Scale Bipartite Rating Graphs Using Lenskit and Spark.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Sardianos, C; Varlamis, I;  and Eirinaki, M\n</span>\n\n  \n\n</span>\n\n  In pages 70–79, April 2017. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1109/BigDataService.2017.28\"\n     onclick=\"log_download('sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017', 'http://dx.doi.org/10.1109/BigDataService.2017.28', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scaling Collaborative Filtering to Large-Scale Bipartite Rating Graphs Using Lenskit and Spark [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/BigDataService.2017.28\"\n     onclick=\"log_download('sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017', 'http://doi.org/10.1109/BigDataService.2017.28', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('sardianos-varlamis-eirinaki-scalingcollaborativefilteringtolargescalebipartiteratinggraphsusinglenskitandspark-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{sardianos_scaling_2017,\n\ttitle = {Scaling {Collaborative} {Filtering} to {Large}-{Scale} {Bipartite} {Rating} {Graphs} {Using} {Lenskit} and {Spark}},\n\turl = {http://dx.doi.org/10.1109/BigDataService.2017.28},\n\tdoi = {10.1109/BigDataService.2017.28},\n\tabstract = {Popular social networking applications such as Facebook, Twitter,\nFriendster, etc. generate very large graphs with different\ncharacteristics. These social networks are huge, comprising millions of\nnodes and edges that push existing graph mining algorithms and\narchitectures to their limits. In product-rating graphs, users connect\nwith each other and rate items in tandem. In such bipartite graphs users\nand items are the nodes and ratings are the edges and collaborative\nfiltering algorithms use the edge information (i.e. user ratings for\nitems) in order to suggest items of potential interest to users. Existing\nalgorithms can hardly scale up to the size of the entire graph and require\nunlimited resources to finish. This work employs a machine learning method\nfor predicting the performance of Collaborative Filtering algorithms using\nthe structural features of the bipartite graphs. Using a fast graph\npartitioning algorithm and information from the user friendship graph, the\noriginal bipartite graph is partitioned into different schemes (i.e. sets\nof smaller bipartite graphs). The schemes are evaluated against the\npredicted performance of the Collaborative Filtering algorithm and the\nbest partitioning scheme is employed for generating the recommendations.\nAs a result, the Collaborative Filtering algorithms are applied to smaller\nbipartite graphs, using limited resources and allowing the problem to\nscale or be parallelized. Tests on a large, real-life, rating graph, show\nthat the proposed method allows the collaborative filtering algorithms to\nrun in parallel and complete using limited resources.},\n\tauthor = {Sardianos, C and Varlamis, I and Eirinaki, M},\n\tmonth = apr,\n\tyear = {2017},\n\tkeywords = {Bipartite graph, Collaboration, Collaborative Filtering, Graph Metrics, Graph Partitioning, Lenskit, Machine learning algorithms, Partitioning algorithms, Prediction algorithms, Recommender Systems, Recommender systems, Social Networks, Social network services, Spark, bipartite graphs, collaborative filtering, collaborative filtering algorithms, data mining, fast graph partitioning algorithm, graph theory, large-scale bipartite rating graphs, learning (artificial intelligence), machine learning, product-rating graphs, social networking (online), social networking applications, structural features, user-friendship graph},\n\tpages = {70--79},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Popular social networking applications such as Facebook, Twitter, Friendster, etc. generate very large graphs with different characteristics. These social networks are huge, comprising millions of nodes and edges that push existing graph mining algorithms and architectures to their limits. In product-rating graphs, users connect with each other and rate items in tandem. In such bipartite graphs users and items are the nodes and ratings are the edges and collaborative filtering algorithms use the edge information (i.e. user ratings for items) in order to suggest items of potential interest to users. Existing algorithms can hardly scale up to the size of the entire graph and require unlimited resources to finish. This work employs a machine learning method for predicting the performance of Collaborative Filtering algorithms using the structural features of the bipartite graphs. Using a fast graph partitioning algorithm and information from the user friendship graph, the original bipartite graph is partitioned into different schemes (i.e. sets of smaller bipartite graphs). The schemes are evaluated against the predicted performance of the Collaborative Filtering algorithm and the best partitioning scheme is employed for generating the recommendations. As a result, the Collaborative Filtering algorithms are applied to smaller bipartite graphs, using limited resources and allowing the problem to scale or be parallelized. Tests on a large, real-life, rating graph, show that the proposed method allows the collaborative filtering algorithms to run in parallel and complete using limited resources.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0957417417301070\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017', 'http://www.sciencedirect.com/science/article/pii/S0957417417301070', event);\">\n    SCoR: A Synthetic Coordinate based Recommender system.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Papadakis, H.; Panagiotakis, C.;  and Fragopoulou, P.\n</span>\n\n  \n\n</span>\n\n  <i>Expert Syst. Appl.</i>, 79: 8–19. August 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0957417417301070\"\n     onclick=\"log_download('papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017', 'http://www.sciencedirect.com/science/article/pii/S0957417417301070', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SCoR: A Synthetic Coordinate based Recommender system [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.eswa.2017.02.025\"\n     onclick=\"log_download('papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017', 'http://doi.org/10.1016/j.eswa.2017.02.025', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('papadakis-panagiotakis-fragopoulou-scorasyntheticcoordinatebasedrecommendersystem-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{papadakis_scor_2017,\n\ttitle = {{SCoR}: {A} {Synthetic} {Coordinate} based {Recommender} system},\n\tvolume = {79},\n\tissn = {0957-4174},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0957417417301070},\n\tdoi = {10.1016/j.eswa.2017.02.025},\n\tabstract = {Recommender systems try to predict the preferences of users for specific\nitems, based on an analysis of previous consumer preferences. In this\npaper, we propose SCoR, a Synthetic Coordinate based Recommendation system\nwhich is shown to outperform the most popular algorithmic techniques in\nthe field, approaches like matrix factorization and collaborative\nfiltering. SCoR assigns synthetic coordinates to nodes (users and items),\nso that the distance between a user and an item provides an accurate\nprediction of the user’s preference for that item. The proposed framework\nhas several benefits. It is parameter free, thus requiring no fine tuning\nto achieve high performance, and is more resistance to the cold-start\nproblem compared to other algorithms. Furthermore, it provides important\nannotations of the dataset, such as the physical detection of users and\nitems with common and unique characteristics as well as the identification\nof outliers. SCoR is compared against nine other state-of-the-art\nrecommender systems, sever of them based on the well known matrix\nfactorization and two on collaborative filtering. The comparison is\nperformed against four real datasets, including a brief version of the\ndataset used in the well known Netflix challenge. The extensive\nexperiments prove that SCoR outperforms previous techniques while\ndemonstrating its improved stability and high performance.},\n\tjournal = {Expert Syst. Appl.},\n\tauthor = {Papadakis, Harris and Panagiotakis, Costas and Fragopoulou, Paraskevi},\n\tmonth = aug,\n\tyear = {2017},\n\tkeywords = {Graph, Matrix factorization, Netflix, Recommender systems, Synthetic coordinates, Vivaldi},\n\tpages = {8--19},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems try to predict the preferences of users for specific items, based on an analysis of previous consumer preferences. In this paper, we propose SCoR, a Synthetic Coordinate based Recommendation system which is shown to outperform the most popular algorithmic techniques in the field, approaches like matrix factorization and collaborative filtering. SCoR assigns synthetic coordinates to nodes (users and items), so that the distance between a user and an item provides an accurate prediction of the user’s preference for that item. The proposed framework has several benefits. It is parameter free, thus requiring no fine tuning to achieve high performance, and is more resistance to the cold-start problem compared to other algorithms. Furthermore, it provides important annotations of the dataset, such as the physical detection of users and items with common and unique characteristics as well as the identification of outliers. SCoR is compared against nine other state-of-the-art recommender systems, sever of them based on the well known matrix factorization and two on collaborative filtering. The comparison is performed against four real datasets, including a brief version of the dataset used in the well known Netflix challenge. The extensive experiments prove that SCoR outperforms previous techniques while demonstrating its improved stability and high performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://hdl.handle.net/10852/59239\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017', 'http://hdl.handle.net/10852/59239', event);\">\n    Video Recommendation Systems: Finding a Suitable Recommendation Approach for an Application Without Sufficient Data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Solvang, M. L.\n</span>\n\n  \n\n</span>\n\n  Master's thesis,  2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://hdl.handle.net/10852/59239\"\n     onclick=\"log_download('solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017', 'http://hdl.handle.net/10852/59239', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Video Recommendation Systems: Finding a Suitable Recommendation Approach for an Application Without Sufficient Data [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('solvang-videorecommendationsystemsfindingasuitablerecommendationapproachforanapplicationwithoutsufficientdata-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@mastersthesis{solvang_video_2017,\n\ttitle = {Video {Recommendation} {Systems}: {Finding} a {Suitable} {Recommendation} {Approach} for an {Application} {Without} {Sufficient} {Data}},\n\turl = {http://hdl.handle.net/10852/59239},\n\tauthor = {Solvang, Marius Lørstad},\n\tyear = {2017},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1002/asi.23978\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017', 'http://dx.doi.org/10.1002/asi.23978', event);\">\n    Recommending books to be exchanged online in the absence of wish lists.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pera, M. S.;  and Ng, Y.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of the Association for Information Science and Technology</i>. November 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1002/asi.23978\"\n     onclick=\"log_download('pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017', 'http://dx.doi.org/10.1002/asi.23978', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommending books to be exchanged online in the absence of wish lists [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1002/asi.23978\"\n     onclick=\"log_download('pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017', 'http://doi.org/10.1002/asi.23978', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pera-ng-recommendingbookstobeexchangedonlineintheabsenceofwishlists-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pera_recommending_2017,\n\ttitle = {Recommending books to be exchanged online in the absence of wish lists},\n\tissn = {2330-1643},\n\turl = {http://dx.doi.org/10.1002/asi.23978},\n\tdoi = {10.1002/asi.23978},\n\tabstract = {An online exchange system is a web service that allows communities to\ntrade items without the burden of manually selecting them, which saves\nusers&#x27; time and effort. Even though online book-exchange systems have been\ndeveloped, their services can further be improved by reducing the workload\nimposed on their users. To accomplish this task, we propose a\nrecommendation-based book exchange system, called EasyEx, which identifies\npotential exchanges for a user solely based on a list of items the user is\nwilling to part with. EasyEx is a novel and unique book-exchange system\nbecause unlike existing online exchange systems, it does not require a\nuser to create and maintain a wish list, which is a list of items the user\nwould like to receive as part of the exchange. Instead, EasyEx directly\nsuggests items to users to increase serendipity and as a result expose\nthem to items which may be unfamiliar, but appealing, to them. In\nidentifying books to be exchanged, EasyEx employs known recommendation\nstrategies, that is, personalized mean and matrix factorization, to\npredict book ratings, which are treated as the degrees of appeal to a user\non recommended books. Furthermore, EasyEx incorporates OptaPlanner, which\nsolves constraint satisfaction problems efficiently, as part of the\nrecommendation-based exchange process to create exchange cycles.\nExperimental results have verified that EasyEx offers users recommended\nbooks that satisfy the users&#x27; interests and contributes to the\nitem-exchange mechanism with a new design methodology.},\n\tjournal = {Journal of the Association for Information Science and Technology},\n\tauthor = {Pera, Maria Soledad and Ng, Yiu-Kai},\n\tmonth = nov,\n\tyear = {2017},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book-exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation-based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book-exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation-based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item-exchange mechanism with a new design methodology.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (9)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016', 'https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf', event);\">\n    rrecsys: An R-package for Prototyping Recommendation Algorithms.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Çoba, L.;  and Zanker, M.\n</span>\n\n  \n\n</span>\n\n  In  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf\"\n     onclick=\"log_download('oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016', 'https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"rrecsys: An R-package for Prototyping Recommendation Algorithms [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('oba-zanker-rrecsysanrpackageforprototypingrecommendationalgorithms-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{coba_rrecsys_2016,\n\ttitle = {rrecsys: {An} {R}-package for {Prototyping} {Recommendation} {Algorithms}},\n\turl = {https://pdfs.semanticscholar.org/1856/b9e4c19a8ed34c3041911e43c0f3f9e1baa5.pdf},\n\tabstract = {ABSTRACT We introduce rrecsys , an open source extension package in R for\nrapid prototyping and intuitive assessment of recommender system\nalgorithms. As the only currently available R package for recommender\nalgorithms (recommenderlab) did not},\n\tauthor = {Çoba, Ludovik and Zanker, Markus},\n\tyear = {2016},\n\tkeywords = {toolkit},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  ABSTRACT We introduce rrecsys , an open source extension package in R for rapid prototyping and intuitive assessment of recommender system algorithms. As the only currently available R package for recommender algorithms (recommenderlab) did not\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://digital.library.txstate.edu/handle/10877/6096\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016', 'https://digital.library.txstate.edu/handle/10877/6096', event);\">\n    A Multi-objective Autotuning Framework For The Java Virtual Machine.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Saha, S.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, Texas State University, April 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://digital.library.txstate.edu/handle/10877/6096\"\n     onclick=\"log_download('saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016', 'https://digital.library.txstate.edu/handle/10877/6096', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Multi-objective Autotuning Framework For The Java Virtual Machine [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('saha-amultiobjectiveautotuningframeworkforthejavavirtualmachine-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{saha_multi-objective_2016,\n\ttitle = {A {Multi}-objective {Autotuning} {Framework} {For} {The} {Java} {Virtual} {Machine}},\n\turl = {https://digital.library.txstate.edu/handle/10877/6096},\n\tabstract = {Due to inherent limitations in performance, Java was not considered a\nsuitable platform for for scalable high-performance computing (HPC) for a\nlong time. The scenario is changing because of the development of\nframeworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in\nusage, achieving high performance with Java is not trivial. High\nperformance in Java relies on libraries providing explicit threads or\nrelying on runnable-like interfaces for distributed programming. In this\nthesis, we develop an autotuning framework for JVM that manages multiple\nobjective functions including execution time, power consumption, energy\nand perfomance-per-watt. The framework searches the combined space of JIT\noptimization sequences and different classes of JVM runtime parameters. To\ndiscover good configurations more quickly, the framework implements novel\nheuristic search algorithms. To reduce the size of the search space\nmachine-learning based pruning techniques are used. Evaluation on\nrecommender system workloads show that significant improvements in both\nperformance and power can be gained by fine-tuning JVM runitme parameters.},\n\turldate = {2016-07-05},\n\tschool = {Texas State University},\n\tauthor = {Saha, Shuvabrata},\n\tmonth = apr,\n\tyear = {2016},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Due to inherent limitations in performance, Java was not considered a suitable platform for for scalable high-performance computing (HPC) for a long time. The scenario is changing because of the development of frameworks like Hadoop, Spark and Fast-MPJ. In spite of the increase in usage, achieving high performance with Java is not trivial. High performance in Java relies on libraries providing explicit threads or relying on runnable-like interfaces for distributed programming. In this thesis, we develop an autotuning framework for JVM that manages multiple objective functions including execution time, power consumption, energy and perfomance-per-watt. The framework searches the combined space of JIT optimization sequences and different classes of JVM runtime parameters. To discover good configurations more quickly, the framework implements novel heuristic search algorithms. To reduce the size of the search space machine-learning based pruning techniques are used. Evaluation on recommender system workloads show that significant improvements in both performance and power can be gained by fine-tuning JVM runitme parameters.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2851581.2892362\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016', 'http://doi.acm.org/10.1145/2851581.2892362', event);\">\n    Evaluating Item-Item Similarity Algorithms for Movies.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Colucci, L.; Doshi, P.; Lee, K.; Liang, J.; Lin, Y.; Vashishtha, I.; Zhang, J.;  and Jude, A.\n</span>\n\n  \n\n</span>\n\n  In <i>CHI EA '16</i>, pages 2141–2147, New York, NY, USA,  2016. ACM\n  <span class=\"note\">Journal Abbreviation: CHI EA '16</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2851581.2892362\"\n     onclick=\"log_download('colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016', 'http://doi.acm.org/10.1145/2851581.2892362', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluating Item-Item Similarity Algorithms for Movies [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2851581.2892362\"\n     onclick=\"log_download('colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016', 'http://doi.org/10.1145/2851581.2892362', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('colucci-doshi-lee-liang-lin-vashishtha-zhang-jude-evaluatingitemitemsimilarityalgorithmsformovies-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{colucci_evaluating_2016,\n\taddress = {New York, NY, USA},\n\ttitle = {Evaluating {Item}-{Item} {Similarity} {Algorithms} for {Movies}},\n\turl = {http://doi.acm.org/10.1145/2851581.2892362},\n\tdoi = {10.1145/2851581.2892362},\n\tbooktitle = {{CHI} {EA} &#x27;16},\n\tpublisher = {ACM},\n\tauthor = {Colucci, Lucas and Doshi, Prachi and Lee, Kun-Lin and Liang, Jiajie and Lin, Yin and Vashishtha, Ishan and Zhang, Jia and Jude, Alvin},\n\tyear = {2016},\n\tnote = {Journal Abbreviation: CHI EA &#x27;16},\n\tkeywords = {algorithm evaluation, item-item similarity, recommender systems},\n\tpages = {2141--2147},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1109/AICCSA.2016.7945792\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016', 'http://dx.doi.org/10.1109/AICCSA.2016.7945792', event);\">\n    Recommendation system based contextual analysis of Facebook comment.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kharrat, F B.; Elkhleifi, A;  and Faiz, R\n</span>\n\n  \n\n</span>\n\n  In pages 1–6, November 2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1109/AICCSA.2016.7945792\"\n     onclick=\"log_download('kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016', 'http://dx.doi.org/10.1109/AICCSA.2016.7945792', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommendation system based contextual analysis of Facebook comment [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/AICCSA.2016.7945792\"\n     onclick=\"log_download('kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016', 'http://doi.org/10.1109/AICCSA.2016.7945792', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kharrat-elkhleifi-faiz-recommendationsystembasedcontextualanalysisoffacebookcomment-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{kharrat_recommendation_2016,\n\ttitle = {Recommendation system based contextual analysis of {Facebook} comment},\n\turl = {http://dx.doi.org/10.1109/AICCSA.2016.7945792},\n\tdoi = {10.1109/AICCSA.2016.7945792},\n\tabstract = {This paper present a new recommendation algorithm based on contextual\nanalysis and new measurements. Social Network is one of the most popular\nWeb 2.0 applications and related services, like Facebook, have evolved\ninto a practical means for sharing opinions. Consequently, Social Network\nweb sites have since become rich data sources for opinion mining. This\npaper proposes to introduce external resource from comments posted by\nusers to predict recommendation and relieve the cold start problem. The\nnovelty of the proposed approach is that posts are not simply\ncharacterized by an opinion score, as is the case with machine\nlearning-based classifiers, but instead receive an opinion grade for each\ndistinct notion in the post. Our approach has been implemented with Java\nand Lenskit framework; the study we have conducted on a movie dataset has\nshown competitive results. We compared our algorithm to SVD and Slope One\nalgorithms. We have obtained an improvement of 8\\% in precision and recall\nas well an improvement of 16\\% in RMSE and nDCG.},\n\tauthor = {Kharrat, F Ben and Elkhleifi, A and Faiz, R},\n\tmonth = nov,\n\tyear = {2016},\n\tkeywords = {Algorithm design and analysis, Classification algorithms, Collaboration, Collaborative filtering, Facebook, Motion pictures, Recommendation system, Recommender systems, Social network, User cold start, User profile},\n\tpages = {1--6},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper present a new recommendation algorithm based on contextual analysis and new measurements. Social Network is one of the most popular Web 2.0 applications and related services, like Facebook, have evolved into a practical means for sharing opinions. Consequently, Social Network web sites have since become rich data sources for opinion mining. This paper proposes to introduce external resource from comments posted by users to predict recommendation and relieve the cold start problem. The novelty of the proposed approach is that posts are not simply characterized by an opinion score, as is the case with machine learning-based classifiers, but instead receive an opinion grade for each distinct notion in the post. Our approach has been implemented with Java and Lenskit framework; the study we have conducted on a movie dataset has shown competitive results. We compared our algorithm to SVD and Slope One algorithms. We have obtained an improvement of 8% in precision and recall as well an improvement of 16% in RMSE and nDCG.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&amp;c=1&amp;af=%5B%5D&amp;searchType=LIST_LATEST&amp;query=&amp;language=en&amp;pid=diva2%3A927356&amp;aq=%5B%5B%5D%5D&amp;sf=all&amp;aqe=%5B%5D&amp;sortOrder=author_sort_asc&amp;onlyFullText=false&amp;noOfRows=50&amp;dswid=-7195\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016', 'http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&amp;c=1&amp;af=%5B%5D&amp;searchType=LIST_LATEST&amp;query=&amp;language=en&amp;pid=diva2%3A927356&amp;aq=%5B%5B%5D%5D&amp;sf=all&amp;aqe=%5B%5D&amp;sortOrder=author_sort_asc&amp;onlyFullText=false&amp;noOfRows=50&amp;dswid=-7195', event);\">\n    Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Salam Patrous, Z.;  and Najafi, S.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, KTH Royal Institute of Technology, Stockholm, Sweden,  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&amp;c=1&amp;af=%5B%5D&amp;searchType=LIST_LATEST&amp;query=&amp;language=en&amp;pid=diva2%3A927356&amp;aq=%5B%5B%5D%5D&amp;sf=all&amp;aqe=%5B%5D&amp;sortOrder=author_sort_asc&amp;onlyFullText=false&amp;noOfRows=50&amp;dswid=-7195\"\n     onclick=\"log_download('salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016', 'http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&amp;c=1&amp;af=%5B%5D&amp;searchType=LIST_LATEST&amp;query=&amp;language=en&amp;pid=diva2%3A927356&amp;aq=%5B%5B%5D%5D&amp;sf=all&amp;aqe=%5B%5D&amp;sortOrder=author_sort_asc&amp;onlyFullText=false&amp;noOfRows=50&amp;dswid=-7195', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('salampatrous-najafi-evaluatingpredictionaccuracyforcollaborativefilteringalgorithmsinrecommendersystems-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{salam_patrous_evaluating_2016,\n\taddress = {Stockholm, Sweden},\n\ttitle = {Evaluating {Prediction} {Accuracy} for {Collaborative} {Filtering} {Algorithms} in {Recommender} {Systems}},\n\turl = {http://kth.diva-portal.org/smash/record.jsf?aq2=%5B%5B%5D%5D&amp;c=1&amp;af=%5B%5D&amp;searchType=LIST_LATEST&amp;query=&amp;language=en&amp;pid=diva2%3A927356&amp;aq=%5B%5B%5D%5D&amp;sf=all&amp;aqe=%5B%5D&amp;sortOrder=author_sort_asc&amp;onlyFullText=false&amp;noOfRows=50&amp;dswid=-7195},\n\tabstract = {Recommender systems are a relatively new technology that is commonly used\nby e-commerce websites and streaming services among others, to predict\nuser opinion about products. This report studies two ...},\n\turldate = {2016-06-13},\n\tschool = {KTH Royal Institute of Technology},\n\tauthor = {Salam Patrous, Ziad and Najafi, Safir},\n\tyear = {2016},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two ...\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chang-leveragingcollectiveintelligenceinrecommendersystem-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://hdl.handle.net/11299/182725\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chang-leveragingcollectiveintelligenceinrecommendersystem-2016', 'http://hdl.handle.net/11299/182725', event);\">\n    Leveraging Collective Intelligence in Recommender System.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chang, S.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Minnesota, Minneapolis, MN, USA, August 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://hdl.handle.net/11299/182725\"\n     onclick=\"log_download('chang-leveragingcollectiveintelligenceinrecommendersystem-2016', 'http://hdl.handle.net/11299/182725', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Leveraging Collective Intelligence in Recommender System [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chang-leveragingcollectiveintelligenceinrecommendersystem-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chang-leveragingcollectiveintelligenceinrecommendersystem-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{chang_leveraging_2016,\n\taddress = {Minneapolis, MN, USA},\n\ttitle = {Leveraging {Collective} {Intelligence} in {Recommender} {System}},\n\turl = {http://hdl.handle.net/11299/182725},\n\tabstract = {Recommender systems, since their introduction 20 years ago, have been\nwidely deployed in web services to alleviate user information overload.\nDriven by business objectives of their applications, the focus of\nrecommender systems has shifted from accurately modeling and predicting\nuser preferences to offering good personalized user experience. The later\nis difficult because there are many factors, e.g., tenure of a user,\ncontext of recommendation and transparency of recommender system, that\naffect users&#x27; perception of recommendations. Many of these factors are\nsubjective and not easily quantifiable, posing challenges to recommender\nalgorithms. When pure algorithmic solutions are at their limits in\nproviding good user experience in recommender systems, we turn to the\ncollective intelligence of human and computer. Computer and human are\ncomplementary to each other: computers are fast at computation and data\nprocessing and have accurate memory; humans are capable of complex\nreasoning, being creative and relating to other humans. In fact, such\nclose collaborations between human and computer have precedent: after\nchess master Garry Kasparov lost to IBM computer &#x60;&#x60;Deep Blue&#x27;&#x27;, he invited\na new form of chess --- advanced chess, in which human player and a\ncomputer program teams up against such pairs. In this thesis, we leverage\nthe collective intelligence of human and computer to tackle several\nchallenges in recommender systems and demonstrate designs of such hybrid\nsystems. We make contributions to the following aspects of recommender\nsystems: providing better new user experience, enhancing topic modeling\ncomponent for items, composing better recommendation sets and generating\npersonalized natural language explanations. These four applications\ndemonstrate different ways of designing systems with collective\nintelligence, applicable to domains other than recommender systems. We\nbelieve the collective intelligence of human and computer can power more\nintelligent, user friendly and creative systems, worthy of continuous\nresearch effort in future.},\n\turldate = {2016-11-01},\n\tschool = {University of Minnesota},\n\tauthor = {Chang, Shuo},\n\tmonth = aug,\n\tyear = {2016},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems, since their introduction 20 years ago, have been widely deployed in web services to alleviate user information overload. Driven by business objectives of their applications, the focus of recommender systems has shifted from accurately modeling and predicting user preferences to offering good personalized user experience. The later is difficult because there are many factors, e.g., tenure of a user, context of recommendation and transparency of recommender system, that affect users' perception of recommendations. Many of these factors are subjective and not easily quantifiable, posing challenges to recommender algorithms. When pure algorithmic solutions are at their limits in providing good user experience in recommender systems, we turn to the collective intelligence of human and computer. Computer and human are complementary to each other: computers are fast at computation and data processing and have accurate memory; humans are capable of complex reasoning, being creative and relating to other humans. In fact, such close collaborations between human and computer have precedent: after chess master Garry Kasparov lost to IBM computer ``Deep Blue'', he invited a new form of chess — advanced chess, in which human player and a computer program teams up against such pairs. In this thesis, we leverage the collective intelligence of human and computer to tackle several challenges in recommender systems and demonstrate designs of such hybrid systems. We make contributions to the following aspects of recommender systems: providing better new user experience, enhancing topic modeling component for items, composing better recommendation sets and generating personalized natural language explanations. These four applications demonstrate different ways of designing systems with collective intelligence, applicable to domains other than recommender systems. We believe the collective intelligence of human and computer can power more intelligent, user friendly and creative systems, worthy of continuous research effort in future.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/article/10.1007/s11042-016-3265-x\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016', 'http://link.springer.com/article/10.1007/s11042-016-3265-x', event);\">\n    Hybrid group recommendations for a travel service.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pessemier, T. D.; Dhondt, J.;  and Martens, L.\n</span>\n\n  \n\n</span>\n\n  <i>Multimed. Tools Appl.</i>, 75(5): 1–25. January 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/article/10.1007/s11042-016-3265-x\"\n     onclick=\"log_download('pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016', 'http://link.springer.com/article/10.1007/s11042-016-3265-x', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Hybrid group recommendations for a travel service [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s11042-016-3265-x\"\n     onclick=\"log_download('pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016', 'http://doi.org/10.1007/s11042-016-3265-x', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pessemier-dhondt-martens-hybridgrouprecommendationsforatravelservice-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pessemier_hybrid_2016,\n\ttitle = {Hybrid group recommendations for a travel service},\n\tvolume = {75},\n\tissn = {1380-7501},\n\turl = {http://link.springer.com/article/10.1007/s11042-016-3265-x},\n\tdoi = {10.1007/s11042-016-3265-x},\n\tabstract = {Recommendation techniques have proven their usefulness as a tool to cope\nwith the information overload problem in many classical domains such as\nmovies, books, and music. Additional challenges for recommender systems\nemerge in the domain of tourism such as acquiring metadata and feedback,\nthe sparsity of the rating matrix, user constraints, and the fact that\ntraveling is often a group activity. This paper proposes a recommender\nsystem that offers personalized recommendations for travel destinations to\nindividuals and groups. These recommendations are based on the users’\nrating profile, personal interests, and specific demands for their next\ndestination. The recommendation algorithm is a hybrid approach combining a\ncontent-based, collaborative filtering, and knowledge-based solution. For\ngroups of users, such as families or friends, individual recommendations\nare aggregated into group recommendations, with an additional opportunity\nfor users to give feedback on these group recommendations. A group of test\nusers evaluated the recommender system using a prototype web application.\nThe results prove the usefulness of individual and group recommendations\nand show that users prefer the hybrid algorithm over each individual\ntechnique. This paper demonstrates the added value of various\nrecommendation algorithms in terms of different quality aspects, compared\nto an unpersonalized list of the most-popular destinations.},\n\tnumber = {5},\n\turldate = {2016-03-11},\n\tjournal = {Multimed. Tools Appl.},\n\tauthor = {Pessemier, Toon De and Dhondt, Jeroen and Martens, Luc},\n\tmonth = jan,\n\tyear = {2016},\n\tpages = {1--25},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users’ rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://hdl.handle.net/11299/182780\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016', 'http://hdl.handle.net/11299/182780', event);\">\n    Enhancing User Experience With Recommender Systems Beyond Prediction Accuracies.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Nguyen, T.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Minnesota, Minneapolis, MN, USA, August 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://hdl.handle.net/11299/182780\"\n     onclick=\"log_download('nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016', 'http://hdl.handle.net/11299/182780', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Enhancing User Experience With Recommender Systems Beyond Prediction Accuracies [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('nguyen-enhancinguserexperiencewithrecommendersystemsbeyondpredictionaccuracies-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{nguyen_enhancing_2016,\n\taddress = {Minneapolis, MN, USA},\n\ttitle = {Enhancing {User} {Experience} {With} {Recommender} {Systems} {Beyond} {Prediction} {Accuracies}},\n\turl = {http://hdl.handle.net/11299/182780},\n\tabstract = {In this dissertation, we examine to improve the user experience with\nrecommender systems beyond prediction accuracy. We focus on the following\naspects of the user experience. In chapter 3 we examine if a recommender\nsystem exposes users to less diverse contents over time. In chapter 4 we\nlook at the relationships between user personality and user preferences\nfor recommendation diversity, popularity, and serendipity. In chapter 5 we\ninvestigate the relations between the self-reported user satisfaction and\nthe three recommendation properties with the inferred user recommendation\nconsumption. In chapter 6 we look at four different rating inter- faces\nand evaluated how these interfaces affected the user rating experience. We\nfind that over time a recommender system exposes users to less-diverse\ncontents and that users rate less-diverse items. However, users who took\nrecommendations were exposed to more diverse recommendations than those\nwho did not. Furthermore, users with different personalities have\ndifferent preferences for recommendation diversity, popularity, and\nserendipity (e.g. some users prefer more diverse recommendations, while\nothers prefer similar ones). We also find that user satisfaction with\nrecommendation popularity and serendipity measured with survey questions\nstrongly relate to user recommendation consumption inferred with logged\ndata. We then propose a way to get better signals about user preferences\nand help users rate items in the recommendation systems more consistently.\nThat is providing exemplars to users at the time they rate the items\nimproved the consistency of users’ ratings. Our results suggest several\nways recommender system practitioners and re- searchers can enrich the\nuser experience. For example, by integrating users’ personality into\nrecommendation frameworks, we can help recommender systems deliver\nrecommendations with the preferred levels of diversity, popularity, and\nserendipity to individual users. We can also facilitate the rating process\nby integrating a set of proven rating-support techniques into the systems’\ninterfaces.},\n\turldate = {2016-11-01},\n\tschool = {University of Minnesota},\n\tauthor = {Nguyen, Tien},\n\tmonth = aug,\n\tyear = {2016},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this dissertation, we examine to improve the user experience with recommender systems beyond prediction accuracy. We focus on the following aspects of the user experience. In chapter 3 we examine if a recommender system exposes users to less diverse contents over time. In chapter 4 we look at the relationships between user personality and user preferences for recommendation diversity, popularity, and serendipity. In chapter 5 we investigate the relations between the self-reported user satisfaction and the three recommendation properties with the inferred user recommendation consumption. In chapter 6 we look at four different rating inter- faces and evaluated how these interfaces affected the user rating experience. We find that over time a recommender system exposes users to less-diverse contents and that users rate less-diverse items. However, users who took recommendations were exposed to more diverse recommendations than those who did not. Furthermore, users with different personalities have different preferences for recommendation diversity, popularity, and serendipity (e.g. some users prefer more diverse recommendations, while others prefer similar ones). We also find that user satisfaction with recommendation popularity and serendipity measured with survey questions strongly relate to user recommendation consumption inferred with logged data. We then propose a way to get better signals about user preferences and help users rate items in the recommendation systems more consistently. That is providing exemplars to users at the time they rate the items improved the consistency of users’ ratings. Our results suggest several ways recommender system practitioners and re- searchers can enrich the user experience. For example, by integrating users’ personality into recommendation frameworks, we can help recommender systems deliver recommendations with the preferred levels of diversity, popularity, and serendipity to individual users. We can also facilitate the rating process by integrating a set of proven rating-support techniques into the systems’ interfaces.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016', 'http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely', event);\">\n    Machine ‘Unlearning’ Technique Wipes Out Unwanted Data Quickly and Completely.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  \n</span>\n\n  \n\n</span>\n\n  March 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely\"\n     onclick=\"log_download('anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016', 'http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Machine ‘Unlearning’ Technique Wipes Out Unwanted Data Quickly and Completely [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('anonymous-machineunlearningtechniquewipesoutunwanteddataquicklyandcompletely-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{noauthor_machine_2016,\n\ttitle = {Machine ‘{Unlearning}’ {Technique} {Wipes} {Out} {Unwanted} {Data} {Quickly} and {Completely}},\n\turl = {http://www.scientificcomputing.com/news/2016/03/machine-unlearning-technique-wipes-out-unwanted-data-quickly-and-completely},\n\tabstract = {Cao and Yang believe that easy adoption of forgetting systems will be\nincreasingly in demand. The pair has developed a way to do it faster and\nmore effectively than what is currently available. Their concept, called\n&quot;machine unlearning,&quot; is so promising that the duo have been awarded a\nfour-year, \\$1.2 million National Science Foundation grant — split between\nLehigh and Columbia — to develop the approach.},\n\turldate = {2016-03-16},\n\tmonth = mar,\n\tyear = {2016},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cao and Yang believe that easy adoption of forgetting systems will be increasingly in demand. The pair has developed a way to do it faster and more effectively than what is currently available. Their concept, called \"machine unlearning,\" is so promising that the duo have been awarded a four-year, $1.2 million National Science Foundation grant — split between Lehigh and Columbia — to develop the approach.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (15)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"harper-konstan-themovielensdatasetshistoryandcontext-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2827872\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'harper-konstan-themovielensdatasetshistoryandcontext-2015', 'http://doi.acm.org/10.1145/2827872', event);\">\n    The MovieLens Datasets: History and Context.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Harper, F M.;  and Konstan, J. A\n</span>\n\n  \n\n</span>\n\n  <i>ACM Transactions on Interactive Intelligent Systems</i>, 5(4): 19:1–19:19. December 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2827872\"\n     onclick=\"log_download('harper-konstan-themovielensdatasetshistoryandcontext-2015', 'http://doi.acm.org/10.1145/2827872', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The MovieLens Datasets: History and Context [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2827872\"\n     onclick=\"log_download('harper-konstan-themovielensdatasetshistoryandcontext-2015', 'http://doi.org/10.1145/2827872', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/harper-konstan-themovielensdatasetshistoryandcontext-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('harper-konstan-themovielensdatasetshistoryandcontext-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{harper_movielens_2015,\n\ttitle = {The {MovieLens} {Datasets}: {History} and {Context}},\n\tvolume = {5},\n\tissn = {2160-6455},\n\turl = {http://doi.acm.org/10.1145/2827872},\n\tdoi = {10.1145/2827872},\n\tabstract = {The MovieLens datasets are widely used in education, research, and\nindustry. They are downloaded hundreds of thousands of times each year,\nreflecting their use in popular press programming books, traditional and\nonline courses, and software. These datasets are a product of member\nactivity in the MovieLens movie recommendation system, an active research\nplatform that has hosted many experiments since its launch in 1997. This\narticle documents the history of MovieLens and the MovieLens datasets. We\ninclude a discussion of lessons learned from running a long-standing, live\nresearch platform from the perspective of a research organization. We\ndocument best practices and limitations of using the MovieLens datasets in\nnew research.},\n\tnumber = {4},\n\turldate = {2016-03-11},\n\tjournal = {ACM Transactions on Interactive Intelligent Systems},\n\tauthor = {Harper, F Maxwell and Konstan, Joseph A},\n\tmonth = dec,\n\tyear = {2015},\n\tkeywords = {dataset},\n\tpages = {19:1--19:19},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2792838.2800179\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015', 'http://doi.acm.org/10.1145/2792838.2800179', event);\">\n    Putting Users in Control of Their Recommendations.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Harper, F M.; Xu, F.; Kaur, H.; Condiff, K.; Chang, S.;  and Terveen, L.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '15</i>, pages 3–10, New York, NY, USA,  2015. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '15</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2792838.2800179\"\n     onclick=\"log_download('harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015', 'http://doi.acm.org/10.1145/2792838.2800179', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Putting Users in Control of Their Recommendations [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2792838.2800179\"\n     onclick=\"log_download('harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015', 'http://doi.org/10.1145/2792838.2800179', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('harper-xu-kaur-condiff-chang-terveen-puttingusersincontroloftheirrecommendations-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{harper_putting_2015,\n\taddress = {New York, NY, USA},\n\ttitle = {Putting {Users} in {Control} of {Their} {Recommendations}},\n\turl = {http://doi.acm.org/10.1145/2792838.2800179},\n\tdoi = {10.1145/2792838.2800179},\n\tabstract = {The essence of a recommender system is that it can recommend items\npersonalized to the preferences of an individual user. But typically users\nare given no explicit control over this personalization, and are instead\nleft guessing about how their actions affect the resulting\nrecommendations. We hypothesize that any recommender algorithm will better\nfit some users&#x27; expectations than others, leaving opportunities for\nimprovement. To address this challenge, we study a recommender that puts\nsome control in the hands of users. Specifically, we build and evaluate a\nsystem that incorporates user-tuned popularity and recency modifiers,\nallowing users to express concepts like &quot;show more popular items&quot;. We find\nthat users who are given these controls evaluate the resulting\nrecommendations much more positively. Further, we find that users diverge\nin their preferred settings, confirming the importance of giving control\nto users.},\n\turldate = {2015-09-19},\n\tbooktitle = {{RecSys} &#x27;15},\n\tpublisher = {ACM},\n\tauthor = {Harper, F Maxwell and Xu, Funing and Kaur, Harmanpreet and Condiff, Kyle and Chang, Shuo and Terveen, Loren},\n\tyear = {2015},\n\tnote = {Journal Abbreviation: RecSys &#x27;15},\n\tpages = {3--10},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like \"show more popular items\". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2675133.2675210\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015', 'http://doi.acm.org/10.1145/2675133.2675210', event);\">\n    Using Groups of Items for Preference Elicitation in Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chang, S.; Harper, F M.;  and Terveen, L.\n</span>\n\n  \n\n</span>\n\n  In <i>CSCW '15</i>, pages 1258–1269, New York, NY, USA,  2015. ACM\n  <span class=\"note\">Journal Abbreviation: CSCW '15</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2675133.2675210\"\n     onclick=\"log_download('chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015', 'http://doi.acm.org/10.1145/2675133.2675210', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Using Groups of Items for Preference Elicitation in Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2675133.2675210\"\n     onclick=\"log_download('chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015', 'http://doi.org/10.1145/2675133.2675210', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chang-harper-terveen-usinggroupsofitemsforpreferenceelicitationinrecommendersystems-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{chang_using_2015,\n\taddress = {New York, NY, USA},\n\ttitle = {Using {Groups} of {Items} for {Preference} {Elicitation} in {Recommender} {Systems}},\n\turl = {http://doi.acm.org/10.1145/2675133.2675210},\n\tdoi = {10.1145/2675133.2675210},\n\tabstract = {To achieve high quality initial personalization, recommender systems must\nprovide an efficient and effective process for new users to express their\npreferences. We propose that this goal is best served not by the classical\nmethod where users begin by expressing preferences for individual items -\nthis process is an inefficient way to convert a user&#x27;s effort into\nimproved personalization. Rather, we propose that new users can begin by\nexpressing their preferences for groups of items. We test this idea by\ndesigning and evaluating an interactive process where users express\npreferences across groups of items that are automatically generated by\nclustering algorithms. We contribute a strategy for recommending items\nbased on these preferences that is generalizable to any collaborative\nfiltering-based system. We evaluate our process with both offline\nsimulation methods and an online user experiment. We find that, as\ncompared with a baseline rate-15-items interface, (a) users are able to\ncomplete the preference elicitation process in less than half the time,\nand (b) users are more satisfied with the resulting recommended items. Our\nevaluation reveals several advantages and other trade-offs involved in\nmoving from item-based preference elicitation to group-based preference\nelicitation.},\n\turldate = {2015-09-19},\n\tbooktitle = {{CSCW} &#x27;15},\n\tpublisher = {ACM},\n\tauthor = {Chang, Shuo and Harper, F Maxwell and Terveen, Loren},\n\tyear = {2015},\n\tnote = {Journal Abbreviation: CSCW &#x27;15},\n\tpages = {1258--1269},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items - this process is an inefficient way to convert a user's effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2792838.2796556\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015', 'http://doi.acm.org/10.1145/2792838.2796556', event);\">\n    Event Recommendation Using Twitter Activity.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Magnuson, A.; Dialani, V.;  and Mallela, D.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '15</i>, pages 331–332, New York, NY, USA,  2015. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '15</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2792838.2796556\"\n     onclick=\"log_download('magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015', 'http://doi.acm.org/10.1145/2792838.2796556', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Event Recommendation Using Twitter Activity [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2792838.2796556\"\n     onclick=\"log_download('magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015', 'http://doi.org/10.1145/2792838.2796556', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('magnuson-dialani-mallela-eventrecommendationusingtwitteractivity-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{magnuson_event_2015,\n\taddress = {New York, NY, USA},\n\ttitle = {Event {Recommendation} {Using} {Twitter} {Activity}},\n\turl = {http://doi.acm.org/10.1145/2792838.2796556},\n\tdoi = {10.1145/2792838.2796556},\n\tabstract = {User interactions with Twitter (social network) frequently take place on\nmobile devices - a user base that it strongly caters to. As much of\nTwitter&#x27;s traffic comes with geo-tagging information associated with it,\nit is a natural platform for geographic recommendations. This paper\nproposes an event recommender system for Twitter users, which identifies\ntwitter activity co-located with previous events, and uses it to drive\ngeographic recommendations via item-based collaborative filtering.},\n\turldate = {2015-09-19},\n\tbooktitle = {{RecSys} &#x27;15},\n\tpublisher = {ACM},\n\tauthor = {Magnuson, Axel and Dialani, Vijay and Mallela, Deepa},\n\tyear = {2015},\n\tnote = {Journal Abbreviation: RecSys &#x27;15},\n\tpages = {331--332},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  User interactions with Twitter (social network) frequently take place on mobile devices - a user base that it strongly caters to. As much of Twitter's traffic comes with geo-tagging information associated with it, it is a natural platform for geographic recommendations. This paper proposes an event recommender system for Twitter users, which identifies twitter activity co-located with previous events, and uses it to drive geographic recommendations via item-based collaborative filtering.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015', 'http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583', event);\">\n    Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ghoshal, A.; Menon, S.;  and Sarkar, S.\n</span>\n\n  \n\n</span>\n\n  <i>Information Systems Research</i>, 26(3): 532–551. July 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583\"\n     onclick=\"log_download('ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015', 'http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1287/isre.2015.0583\"\n     onclick=\"log_download('ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015', 'http://doi.org/10.1287/isre.2015.0583', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghoshal-menon-sarkar-recommendationsusinginformationfrommultipleassociationrulesaprobabilisticapproach-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{ghoshal_recommendations_2015,\n\ttitle = {Recommendations {Using} {Information} from {Multiple} {Association} {Rules}: {A} {Probabilistic} {Approach}},\n\tvolume = {26},\n\tissn = {1047-7047},\n\turl = {http://pubsonline.informs.org/doi/abs/10.1287/isre.2015.0583},\n\tdoi = {10.1287/isre.2015.0583},\n\tabstract = {Business analytics has evolved from being a novelty used by a select few\nto an accepted facet of conducting business. Recommender systems form a\ncritical component of the business analytics toolkit and, by enabling\nfirms to effectively target customers with products and services, are\nhelping alter the e-commerce landscape. A variety of methods exist for\nproviding recommendations, with collaborative filtering, matrix\nfactorization, and association-rule-based methods being the most common.\nIn this paper, we propose a method to improve the quality of\nrecommendations made using association rules. This is accomplished by\ncombining rules when possible and stands apart from existing\nrule-combination methods in that it is strongly grounded in probability\ntheory. Combining rules requires the identification of the best\ncombination of rules from the many combinations that might exist, and we\nuse a maximum-likelihood framework to compare alternative combinations.\nBecause it is impractical to apply the maximum likelihood framework\ndirectly in real time, we show that this problem can equivalently be\nrepresented as a set partitioning problem by translating it into an\ninformation theoretic context—the best solution corresponds to the set of\nrules that leads to the highest sum of mutual information associated with\nthe rules. Through a variety of experiments that evaluate the quality of\nrecommendations made using the proposed approach, we show that (i) a\ngreedy heuristic used to solve the maximum likelihood estimation problem\nis very effective, providing results comparable to those from using the\noptimal set partitioning solution; (ii) the recommendations made by our\napproach are more accurate than those made by a variety of\nstate-of-the-art benchmarks, including collaborative filtering and matrix\nfactorization; and (iii) the recommendations can be made in a fraction of\na second on a desktop computer, making it practical to use in real-world\napplications.},\n\tnumber = {3},\n\turldate = {2015-09-19},\n\tjournal = {Information Systems Research},\n\tauthor = {Ghoshal, Abhijeet and Menon, Syam and Sarkar, Sumit},\n\tmonth = jul,\n\tyear = {2015},\n\tpages = {532--551},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Business analytics has evolved from being a novelty used by a select few to an accepted facet of conducting business. Recommender systems form a critical component of the business analytics toolkit and, by enabling firms to effectively target customers with products and services, are helping alter the e-commerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association-rule-based methods being the most common. In this paper, we propose a method to improve the quality of recommendations made using association rules. This is accomplished by combining rules when possible and stands apart from existing rule-combination methods in that it is strongly grounded in probability theory. Combining rules requires the identification of the best combination of rules from the many combinations that might exist, and we use a maximum-likelihood framework to compare alternative combinations. Because it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context—the best solution corresponds to the set of rules that leads to the highest sum of mutual information associated with the rules. Through a variety of experiments that evaluate the quality of recommendations made using the proposed approach, we show that (i) a greedy heuristic used to solve the maximum likelihood estimation problem is very effective, providing results comparable to those from using the optimal set partitioning solution; (ii) the recommendations made by our approach are more accurate than those made by a variety of state-of-the-art benchmarks, including collaborative filtering and matrix factorization; and (iii) the recommendations can be made in a fraction of a second on a desktop computer, making it practical to use in real-world applications.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wischenbart-firmenich-rossi-wimmer-recommendersystemsforthepeopleenhancingpersonalizationinwebaugmentation-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Recommender Systems for the People — Enhancing Personalization in Web Augmentation.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wischenbart, M.; Firmenich, S.; Rossi, G.;  and Wimmer, M.\n</span>\n\n  \n\n</span>\n\n  In September 2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wischenbart-firmenich-rossi-wimmer-recommendersystemsforthepeopleenhancingpersonalizationinwebaugmentation-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wischenbart-firmenich-rossi-wimmer-recommendersystemsforthepeopleenhancingpersonalizationinwebaugmentation-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{wischenbart_recommender_2015,\n\ttitle = {Recommender {Systems} for the {People} — {Enhancing} {Personalization} in {Web} {Augmentation}},\n\tauthor = {Wischenbart, Martin and Firmenich, Sergio and Rossi, Gustavo and Wimmer, Manuel},\n\tmonth = sep,\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015', 'http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1', event);\">\n    BoostMF: Boosted Matrix Factorisation for Collaborative Ranking.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chowdhury, N.; Cai, X.;  and Luo, C.\n</span>\n\n  \n\n</span>\n\n  In Appice, A.; Rodrigues, P. P.; Costa, V. S.; Gama, J.; Jorge, A.;  and Soares, C., editor(s), <i>Machine Learning and Knowledge Discovery in Databases</i>, of Lecture Notes in Computer Science, pages 3–18. Springer International Publishing, September 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1\"\n     onclick=\"log_download('chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015', 'http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"BoostMF: Boosted Matrix Factorisation for Collaborative Ranking [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chowdhury-cai-luo-boostmfboostedmatrixfactorisationforcollaborativeranking-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{chowdhury_boostmf_2015,\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {{BoostMF}: {Boosted} {Matrix} {Factorisation} for {Collaborative} {Ranking}},\n\tisbn = {978-3-319-23524-0},\n\turl = {http://link.springer.com/chapter/10.1007/978-3-319-23525-7_1},\n\turldate = {2015-09-19},\n\tbooktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Chowdhury, Nipa and Cai, Xiongcai and Luo, Cheng},\n\teditor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Costa, Vítor Santos and Gama, João and Jorge, Alípio and Soares, Carlos},\n\tmonth = sep,\n\tyear = {2015},\n\tpages = {3--18},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015', 'http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48', event);\">\n    Stream-Based Recommendations: Online and Offline Evaluation as a Service.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kille, B.; Lommatzsch, A.; Turrin, R.; Serény, A.; Larson, M.; Brodt, T.; Seiler, J.;  and Hopfgartner, F.\n</span>\n\n  \n\n</span>\n\n  In Mothe, J.; Savoy, J.; Kamps, J.; Pinel-Sauvagnat, K.; Jones, G. J F; SanJuan, E.; Cappellato, L.;  and Ferro, N., editor(s), <i>Experimental IR Meets Multilinguality, Multimodality, and Interaction</i>, of Lecture Notes in Computer Science, pages 497–517. Springer International Publishing,  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48\"\n     onclick=\"log_download('kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015', 'http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Stream-Based Recommendations: Online and Offline Evaluation as a Service [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kille-lommatzsch-turrin-serny-larson-brodt-seiler-hopfgartner-streambasedrecommendationsonlineandofflineevaluationasaservice-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{kille_stream-based_2015,\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {Stream-{Based} {Recommendations}: {Online} and {Offline} {Evaluation} as a {Service}},\n\tisbn = {978-3-319-24026-8},\n\turl = {http://link.springer.com/chapter/10.1007/978-3-319-24027-5_48},\n\turldate = {2015-09-19},\n\tbooktitle = {Experimental {IR} {Meets} {Multilinguality}, {Multimodality}, and {Interaction}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Kille, Benjamin and Lommatzsch, Andreas and Turrin, Roberto and Serény, András and Larson, Martha and Brodt, Torben and Seiler, Jonas and Hopfgartner, Frank},\n\teditor = {Mothe, Josiane and Savoy, Jacques and Kamps, Jaap and Pinel-Sauvagnat, Karen and Jones, Gareth J F and SanJuan, Eric and Cappellato, Linda and Ferro, Nicola},\n\tyear = {2015},\n\tpages = {497--517},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015', 'http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf', event);\">\n    Recommender Systems; Contextual Multi-Armed Bandit Algorithms for the purpose of targeted advertisement within e-commerce.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ek, F.;  and Stigsson, R.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, Chalmers University of Technology, Gothenburg, Sweden,  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf\"\n     onclick=\"log_download('ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015', 'http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Recommender Systems; Contextual Multi-Armed Bandit Algorithms for the purpose of targeted advertisement within e-commerce [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ek-stigsson-recommendersystemscontextualmultiarmedbanditalgorithmsforthepurposeoftargetedadvertisementwithinecommerce-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{ek_recommender_2015,\n\taddress = {Gothenburg, Sweden},\n\ttitle = {Recommender {Systems}; {Contextual} {Multi}-{Armed} {Bandit} {Algorithms} for the purpose of targeted advertisement within e-commerce},\n\turl = {http://publications.lib.chalmers.se/records/fulltext/219662/219662.pdf},\n\tschool = {Chalmers University of Technology},\n\tauthor = {Ek, Frederik and Stigsson, Robert},\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015', 'http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z', event);\">\n    AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Christou, I. T; Amolochitis, E.;  and Tan, Z.\n</span>\n\n  \n\n</span>\n\n  <i>Knowl. Inf. Syst.</i>,1–26. August 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z\"\n     onclick=\"log_download('christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015', 'http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10115-015-0866-z\"\n     onclick=\"log_download('christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015', 'http://doi.org/10.1007/s10115-015-0866-z', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('christou-amolochitis-tan-amoredesignandimplementationofacommercialstrengthparallelhybridmovierecommendationengine-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{christou_amore_2015,\n\ttitle = {{AMORE}: design and implementation of a commercial-strength parallel hybrid movie recommendation engine},\n\tissn = {0219-1377},\n\turl = {http://link.springer.com.libproxy.txstate.edu/article/10.1007/s10115-015-0866-z},\n\tdoi = {10.1007/s10115-015-0866-z},\n\turldate = {2015-09-19},\n\tjournal = {Knowl. Inf. Syst.},\n\tauthor = {Christou, Ioannis T and Amolochitis, Emmanouil and Tan, Zheng-Hua},\n\tmonth = aug,\n\tyear = {2015},\n\tpages = {1--26},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015', 'http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf', event);\">\n    Towards Making Systems Forget with Machine Unlearning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cao, Y.;  and Yang, J.\n</span>\n\n  \n\n</span>\n\n  In May 2015. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf\"\n     onclick=\"log_download('cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015', 'http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Towards Making Systems Forget with Machine Unlearning [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cao-yang-towardsmakingsystemsforgetwithmachineunlearning-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{cao_towards_2015,\n\ttitle = {Towards {Making} {Systems} {Forget} with {Machine} {Unlearning}},\n\turl = {http://www.ieee-security.org/TC/SP2015/papers-archived/6949a463.pdf},\n\tabstract = {Today’s systems produce a wealth of data every day, and the data further\ngenerates more data, i.e., the derived data, forming into a complex data\npropagation network, defined as the data’s lineage. There are many reasons\nfor users and administrators to forget certain data including the data’s\nlineage. From the privacy perspective, a system may leak private\ninformation of certain users, and those users unhappy about privacy leaks\nnaturally want to forget their data and its lineage. From the security\nperspective, an anomaly detection system can be polluted by adversaries\nthrough injecting manually crafted data into the training set. Therefore,\nwe envision forgetting systems, capable of completely forgetting certain\ndata and its lineage. In this paper, we focus on making learning systems\nforget, the process of which is defined as machine unlearning or\nunlearning. To perform unlearning upon learning system, we present general\nunlearning criteria, i.e., converting a learning system or part of it into\na summation form of statistical query learning model, and updating all the\nsummations to achieve unlearning. Then, we integrate our unlearning\ncriteria into an unlearning architecture that interacts with all the\ncomponents of a learning system, such as sample clustering and feature\nselection. To demonstrate our unlearning criteria and architecture, we\nselect four real-world learning systems, including an item-item\nrecommendation system, an online social network spam filter, and a malware\ndetection system. These systems are first exposed to an adversarial\nenvironment, e.g., if the system is potentially vulnerable to training\ndata pollution, we first pollute the training data set and show that the\ndetection rate drops significantly. Then, we apply our unlearning\ntechnique upon those affected systems, either polluted or leaking private\ninformation. Our results show that after unlearning, the detection rate of\na polluted system increases back to the one before pollution, and a system\nleaking a particular user’s private information completely forgets that\ninformation.},\n\tpublisher = {IEEE},\n\tauthor = {Cao, Yinzhi and Yang, Junfeng},\n\tmonth = may,\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Today’s systems produce a wealth of data every day, and the data further generates more data, i.e., the derived data, forming into a complex data propagation network, defined as the data’s lineage. There are many reasons for users and administrators to forget certain data including the data’s lineage. From the privacy perspective, a system may leak private information of certain users, and those users unhappy about privacy leaks naturally want to forget their data and its lineage. From the security perspective, an anomaly detection system can be polluted by adversaries through injecting manually crafted data into the training set. Therefore, we envision forgetting systems, capable of completely forgetting certain data and its lineage. In this paper, we focus on making learning systems forget, the process of which is defined as machine unlearning or unlearning. To perform unlearning upon learning system, we present general unlearning criteria, i.e., converting a learning system or part of it into a summation form of statistical query learning model, and updating all the summations to achieve unlearning. Then, we integrate our unlearning criteria into an unlearning architecture that interacts with all the components of a learning system, such as sample clustering and feature selection. To demonstrate our unlearning criteria and architecture, we select four real-world learning systems, including an item-item recommendation system, an online social network spam filter, and a malware detection system. These systems are first exposed to an adversarial environment, e.g., if the system is potentially vulnerable to training data pollution, we first pollute the training data set and show that the detection rate drops significantly. Then, we apply our unlearning technique upon those affected systems, either polluted or leaking private information. Our results show that after unlearning, the detection rate of a polluted system increases back to the one before pollution, and a system leaking a particular user’s private information completely forgets that information.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015', 'http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69', event);\">\n    Recommendation Systems Based on Online User's Action.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Elkhelifi, A; Kharrat, F B.;  and Faiz, R\n</span>\n\n  \n\n</span>\n\n  In pages 485–490, October 2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69\"\n     onclick=\"log_download('elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015', 'http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommendation Systems Based on Online User&#x27;s Action [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69\"\n     onclick=\"log_download('elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015', 'http://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('elkhelifi-kharrat-faiz-recommendationsystemsbasedononlineusersaction-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{elkhelifi_recommendation_2015,\n\ttitle = {Recommendation {Systems} {Based} on {Online} {User}&#x27;s {Action}},\n\turl = {http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.69},\n\tdoi = {10.1109/CIT/IUCC/DASC/PICOM.2015.69},\n\tabstract = {In this paper, we propose a new recommender algorithm based on\nmulti-dimensional users behavior and new measurements. It&#x27;s used in the\nframework of our recommender system that use knowledge discovery\ntechniques to the problem of making product recommendations during a live\nuser interaction. Most of Collaborative filtering algorithms based on\nuser&#x27;s rating or similar item that other users bought, we propose to\ncombine all user&#x27;s action to predict recommendation. These systems are\nachieving widespread success in E-tourism nowadays. We evaluate our\nalgorithm on tourism dataset. Evaluations have shown good results. We\ncompared our algorithm to Slope One and Weight Slope One. We obtained an\nimprovement of 5\\% in precision and recall. And an improvement of 12\\% in\nRMSE and nDCG.},\n\tauthor = {Elkhelifi, A and Kharrat, F Ben and Faiz, R},\n\tmonth = oct,\n\tyear = {2015},\n\tpages = {485--490},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper, we propose a new recommender algorithm based on multi-dimensional users behavior and new measurements. It's used in the framework of our recommender system that use knowledge discovery techniques to the problem of making product recommendations during a live user interaction. Most of Collaborative filtering algorithms based on user's rating or similar item that other users bought, we propose to combine all user's action to predict recommendation. These systems are achieving widespread success in E-tourism nowadays. We evaluate our algorithm on tourism dataset. Evaluations have shown good results. We compared our algorithm to Slope One and Weight Slope One. We obtained an improvement of 5% in precision and recall. And an improvement of 12% in RMSE and nDCG.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dragovic-pera-exploitingreviewstoguideusersselections-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'dragovic-pera-exploitingreviewstoguideusersselections-2015', 'http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf', event);\">\n    Exploiting Reviews to Guide Users’ Selections.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dragovic, N.;  and Pera, M. S.\n</span>\n\n  \n\n</span>\n\n  In  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf\"\n     onclick=\"log_download('dragovic-pera-exploitingreviewstoguideusersselections-2015', 'http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Exploiting Reviews to Guide Users’ Selections [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dragovic-pera-exploitingreviewstoguideusersselections-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dragovic-pera-exploitingreviewstoguideusersselections-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{dragovic_exploiting_2015,\n\ttitle = {Exploiting {Reviews} to {Guide} {Users}’ {Selections}},\n\turl = {http://ceur-ws.org/Vol-1441/recsys2015_poster7.pdf},\n\turldate = {2017-03-01},\n\tauthor = {Dragovic, Nevena and Pera, Maria Soledad},\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015', 'http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf', event);\">\n    Towards Improving Top-N Recommendation by Generalization of SLIM.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Larraín, S.; Parra, D.;  and Soto, A.\n</span>\n\n  \n\n</span>\n\n  In  2015. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf\"\n     onclick=\"log_download('larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015', 'http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Towards Improving Top-N Recommendation by Generalization of SLIM [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('larran-parra-soto-towardsimprovingtopnrecommendationbygeneralizationofslim-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{larrain_towards_2015,\n\ttitle = {Towards {Improving} {Top}-{N} {Recommendation} by {Generalization} of {SLIM}},\n\turl = {http://ceur-ws.org/Vol-1441/recsys2015_poster22.pdf},\n\tauthor = {Larraín, Santiago and Parra, Denis and Soto, Alvaro},\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"dhondt-ahybridgrouprecommendersystemfortraveldestinations-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A hybrid group recommender system for travel destinations.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dhondt, J.\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Gent, Gent, Belgium, May 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dhondt-ahybridgrouprecommendersystemfortraveldestinations-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dhondt-ahybridgrouprecommendersystemfortraveldestinations-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{dhondt_hybrid_2015,\n\taddress = {Gent, Belgium},\n\ttitle = {A hybrid group recommender system for travel destinations},\n\tschool = {University of Gent},\n\tauthor = {Dhondt, Jeroen},\n\tmonth = may,\n\tyear = {2015},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (9)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645746\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014', 'http://dx.doi.org/10.1145/2645710.2645746', event);\">\n    Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Said, A.;  and Bellogin, A.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '14</i>, pages 129–136, New York, NY, USA, October 2014. ACM Press\n  <span class=\"note\">Journal Abbreviation: RecSys '14</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645746\"\n     onclick=\"log_download('said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014', 'http://dx.doi.org/10.1145/2645710.2645746', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2645710.2645746\"\n     onclick=\"log_download('said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014', 'http://doi.org/10.1145/2645710.2645746', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('said-bellogin-comparativerecommendersystemevaluationbenchmarkingrecommendationframeworks-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{said_comparative_2014,\n\taddress = {New York, NY, USA},\n\ttitle = {Comparative {Recommender} {System} {Evaluation}: {Benchmarking} {Recommendation} {Frameworks}},\n\turl = {http://dx.doi.org/10.1145/2645710.2645746},\n\tdoi = {10.1145/2645710.2645746},\n\tabstract = {Recommender systems research is often based on comparisons of predictive\naccuracy: the better the evaluation scores, the better the recommender.\nHowever, it is difficult to compare results from different recommender\nsystems due to the many options in design and implementation of an\nevaluation strategy. Additionally, algorithmic implementations can diverge\nfrom the standard formulation due to manual tuning and modifications that\nwork better in some situations. In this work we compare common\nrecommendation algorithms as implemented in three popular recommendation\nframeworks. To provide a fair comparison, we have complete control of the\nevaluation dimensions being benchmarked: dataset, data splitting,\nevaluation strategies, and metrics. We also include results using the\ninternal evaluation mechanisms of these frameworks. Our analysis points to\nlarge differences in recommendation accuracy across frameworks and\nstrategies, i.e. the same baselines may perform orders of magnitude better\nor worse across frameworks. Our results show the necessity of clear\nguidelines when reporting evaluation of recommender systems to ensure\nreproducibility and comparison of results.},\n\turldate = {2017-02-03},\n\tbooktitle = {{RecSys} &#x27;14},\n\tpublisher = {ACM Press},\n\tauthor = {Said, Alan and Bellogin, Alejandro},\n\tmonth = oct,\n\tyear = {2014},\n\tnote = {Journal Abbreviation: RecSys &#x27;14},\n\tkeywords = {toolkit},\n\tpages = {129--136},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from different recommender systems due to the many options in design and implementation of an evaluation strategy. Additionally, algorithmic implementations can diverge from the standard formulation due to manual tuning and modifications that work better in some situations. In this work we compare common recommendation algorithms as implemented in three popular recommendation frameworks. To provide a fair comparison, we have complete control of the evaluation dimensions being benchmarked: dataset, data splitting, evaluation strategies, and metrics. We also include results using the internal evaluation mechanisms of these frameworks. Our analysis points to large differences in recommendation accuracy across frameworks and strategies, i.e. the same baselines may perform orders of magnitude better or worse across frameworks. Our results show the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://hdl.handle.net/11299/165307\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014', 'http://hdl.handle.net/11299/165307', event);\">\n    Towards Recommender Engineering: Tools and Experiments in Recommender Differences.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D\n</span>\n\n  \n\n</span>\n\n  Ph.D. Thesis, University of Minnesota, Minneapolis, MN, July 2014.\n  <span class=\"note\">Publication Title: Computer Science and Engineering Volume: Ph.D</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://hdl.handle.net/11299/165307\"\n     onclick=\"log_download('ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014', 'http://hdl.handle.net/11299/165307', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards Recommender Engineering: Tools and Experiments in Recommender Differences [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-towardsrecommenderengineeringtoolsandexperimentsinrecommenderdifferences-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@phdthesis{ekstrand_towards_2014,\n\taddress = {Minneapolis, MN},\n\ttitle = {Towards {Recommender} {Engineering}: {Tools} and {Experiments} in {Recommender} {Differences}},\n\turl = {http://hdl.handle.net/11299/165307},\n\tabstract = {Since the introduction of their modern form 20 years ago, recommender\nsystems have proven a valuable tool for help users manage information\noverload. Two decades of research have produced many algorithms for\ncomputing recommendations, mechanisms for evaluating their effectiveness,\nand user interfaces and experiences to embody them. It has also been found\nthat the outputs of different recommendation algorithms differ in\nuser-perceptible ways that affect their suitability to different tasks and\ninformation needs. However, there has been little work to systematically\nmap out the space of algorithms and the characteristics they exhibit that\nmakes them more or less effective in different applications. As a result,\ndevelopers of recommender systems must experiment, conducting basic\nscience on each application and its users to determine the approach(es)\nthat will meet their needs. This thesis presents our work towards\nrecommender engineering: the design of recommender systems from\nwell-understood principles of user needs, domain properties, and algorithm\nbehaviors. This will reduce the experimentation required for each new\nrecommender application, allowing developers to design recommender systems\nthat are likely to be effective for their particular application. To that\nend, we make four contributions: the LensKit toolkit for conducting\nexperiments on a wide variety of recommender algorithms and data sets\nunder different experimental conditions (offline experiments with diverse\nmetrics, online user studies, and the ability to grow to support\nadditional methodologies), along with new developments in object-oriented\nsoftware configuration to support this toolkit; experiments on the\nconfiguration options of widely-used algorithms to provide guidance on\ntuning and configuring them; an offline experiment on the differences in\nthe errors made by different algorithms; and a user study on the\nuser-perceptible differences between lists of movie recommendations\nproduced by three common recommender algorithms. Much research is needed\nto fully realize the vision of recommender engineering in the coming\nyears; it is our hope that LensKit will prove a valuable foundation for\nmuch of this work, and our experiments represent a small piece of the\nkinds of studies that must be carried out, replicated, and validated to\nenable recommender systems to be engineered.},\n\tschool = {University of Minnesota},\n\tauthor = {Ekstrand, Michael D},\n\tcollaborator = {Konstan, Joseph A},\n\tmonth = jul,\n\tyear = {2014},\n\tnote = {Publication Title: Computer Science and Engineering\nVolume: Ph.D},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Since the introduction of their modern form 20 years ago, recommender systems have proven a valuable tool for help users manage information overload. Two decades of research have produced many algorithms for computing recommendations, mechanisms for evaluating their effectiveness, and user interfaces and experiences to embody them. It has also been found that the outputs of different recommendation algorithms differ in user-perceptible ways that affect their suitability to different tasks and information needs. However, there has been little work to systematically map out the space of algorithms and the characteristics they exhibit that makes them more or less effective in different applications. As a result, developers of recommender systems must experiment, conducting basic science on each application and its users to determine the approach(es) that will meet their needs. This thesis presents our work towards recommender engineering: the design of recommender systems from well-understood principles of user needs, domain properties, and algorithm behaviors. This will reduce the experimentation required for each new recommender application, allowing developers to design recommender systems that are likely to be effective for their particular application. To that end, we make four contributions: the LensKit toolkit for conducting experiments on a wide variety of recommender algorithms and data sets under different experimental conditions (offline experiments with diverse metrics, online user studies, and the ability to grow to support additional methodologies), along with new developments in object-oriented software configuration to support this toolkit; experiments on the configuration options of widely-used algorithms to provide guidance on tuning and configuring them; an offline experiment on the differences in the errors made by different algorithms; and a user study on the user-perceptible differences between lists of movie recommendations produced by three common recommender algorithms. Much research is needed to fully realize the vision of recommender engineering in the coming years; it is our hope that LensKit will prove a valuable foundation for much of this work, and our experiments represent a small piece of the kinds of studies that must be carried out, replicated, and validated to enable recommender systems to be engineered.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2556325.2566244\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014', 'http://doi.acm.org/10.1145/2556325.2566244', event);\">\n    Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Konstan, J. A; Walker, J D; Brooks, D C.; Brown, K.;  and Ekstrand, M. D\n</span>\n\n  \n\n</span>\n\n  In <i>L@S '14</i>, pages 61–70, New York, NY, USA, March 2014. ACM\n  <span class=\"note\">Journal Abbreviation: L@S '14</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2556325.2566244\"\n     onclick=\"log_download('konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014', 'http://doi.acm.org/10.1145/2556325.2566244', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2556325.2566244\"\n     onclick=\"log_download('konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014', 'http://doi.org/10.1145/2556325.2566244', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('konstan-walker-brooks-brown-ekstrand-teachingrecommendersystemsatlargescaleevaluationandlessonslearnedfromahybridmooc-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{konstan_teaching_2014,\n\taddress = {New York, NY, USA},\n\ttitle = {Teaching {Recommender} {Systems} at {Large} {Scale}: {Evaluation} and {Lessons} {Learned} from a {Hybrid} {MOOC}},\n\turl = {http://doi.acm.org/10.1145/2556325.2566244},\n\tdoi = {10.1145/2556325.2566244},\n\tabstract = {In Fall 2013 we offered an open online Introduction to Recommender Systems\nthrough Coursera, while simultaneously offering a for-credit version of\nthe course on-campus using the Coursera platform and a flipped classroom\ninstruction model. As the goal of offering this course was to experiment\nwith this type of instruction, we performed extensive evaluation including\nsurveys of demographics, self-assessed skills, and learning intent; we\nalso designed a knowledge-assessment tool specifically for the subject\nmatter in this course, administering it before and after the course to\nmeasure learning. We also tracked students through the course, including\nseparating out students enrolled for credit from those enrolled only for\nthe free, open course. This article reports on our findings.},\n\turldate = {2014-03-19},\n\tbooktitle = {L@{S} &#x27;14},\n\tpublisher = {ACM},\n\tauthor = {Konstan, Joseph A and Walker, J D and Brooks, D Christopher and Brown, Keith and Ekstrand, Michael D},\n\tmonth = mar,\n\tyear = {2014},\n\tnote = {Journal Abbreviation: L@S &#x27;14},\n\tpages = {61--70},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645737\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014', 'http://dx.doi.org/10.1145/2645710.2645737', event);\">\n    User Perception of Differences in Movie Recommendation Algorithms.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D; Harper, F M.; Willemsen, M. C;  and Konstan, J. A\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '14</i>, pages 161–168, New York, NY, USA, October 2014. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '14</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645737\"\n     onclick=\"log_download('ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014', 'http://dx.doi.org/10.1145/2645710.2645737', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"User Perception of Differences in Movie Recommendation Algorithms [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2645710.2645737\"\n     onclick=\"log_download('ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014', 'http://doi.org/10.1145/2645710.2645737', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-harper-willemsen-konstan-userperceptionofdifferencesinmovierecommendationalgorithms-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ekstrand_user_2014,\n\taddress = {New York, NY, USA},\n\ttitle = {User {Perception} of {Differences} in {Movie} {Recommendation} {Algorithms}},\n\turl = {http://dx.doi.org/10.1145/2645710.2645737},\n\tdoi = {10.1145/2645710.2645737},\n\tabstract = {Recommender systems research is often based on comparisons of predictive\naccuracy: the better the evaluation scores, the better the recommender.\nHowever, it is difficult to compare results from different recommender\nsystems due to the many options in design and implementation of an\nevaluation strategy. Additionally, algorithm implementations can diverge\nfrom the standard formulation due to manual tuning and modifications that\nwork better in some situations. In this work we compare common\nrecommendation algorithms as implemented in three popular recommendation\nframeworks. To provide a fair comparison, we have complete control of the\nevaluation dimensions being benchmarked: dataset, data splitting,\nevaluation strategies, and metrics. We also include results using the\ninternal evaluation mechanisms of these frameworks. Our analysis points to\nlarge differences in recommendation accuracy across frameworks and\nstrategies, i.e. the same baselines may perform orders of magnitude better\nor worse across frameworks. Our results show the necessity of clear\nguidelines when reporting evaluation of recommender systems to ensure\nreproducibility and comparison of results.},\n\tbooktitle = {{RecSys} &#x27;14},\n\tpublisher = {ACM},\n\tauthor = {Ekstrand, Michael D and Harper, F Maxwell and Willemsen, Martijn C and Konstan, Joseph A},\n\tmonth = oct,\n\tyear = {2014},\n\tnote = {Journal Abbreviation: RecSys &#x27;14},\n\tpages = {161--168},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender systems research is often based on comparisons of predictive accuracy: the better the evaluation scores, the better the recommender. However, it is difficult to compare results from different recommender systems due to the many options in design and implementation of an evaluation strategy. Additionally, algorithm implementations can diverge from the standard formulation due to manual tuning and modifications that work better in some situations. In this work we compare common recommendation algorithms as implemented in three popular recommendation frameworks. To provide a fair comparison, we have complete control of the evaluation dimensions being benchmarked: dataset, data splitting, evaluation strategies, and metrics. We also include results using the internal evaluation mechanisms of these frameworks. Our analysis points to large differences in recommendation accuracy across frameworks and strategies, i.e. the same baselines may perform orders of magnitude better or worse across frameworks. Our results show the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645742\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014', 'http://dx.doi.org/10.1145/2645710.2645742', event);\">\n    Evaluating Recommender Behavior for New Users.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kluver, D.;  and Konstan, J. A\n</span>\n\n  \n\n</span>\n\n  In October 2014. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1145/2645710.2645742\"\n     onclick=\"log_download('kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014', 'http://dx.doi.org/10.1145/2645710.2645742', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Evaluating Recommender Behavior for New Users [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2645710.2645742\"\n     onclick=\"log_download('kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014', 'http://doi.org/10.1145/2645710.2645742', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kluver-konstan-evaluatingrecommenderbehaviorfornewusers-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{kluver_evaluating_2014,\n\ttitle = {Evaluating {Recommender} {Behavior} for {New} {Users}},\n\turl = {http://dx.doi.org/10.1145/2645710.2645742},\n\tdoi = {10.1145/2645710.2645742},\n\tpublisher = {ACM},\n\tauthor = {Kluver, Daniel and Konstan, Joseph A},\n\tmonth = oct,\n\tyear = {2014},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1877050914013751\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014', 'http://www.sciencedirect.com/science/article/pii/S1877050914013751', event);\">\n    A Personalized Concept-driven Recommender System for Scientific Libraries.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  De Nart, D;  and Tasso, C\n</span>\n\n  \n\n</span>\n\n  <i>Procedia Comput. Sci.</i>, 38: 84–91.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1877050914013751\"\n     onclick=\"log_download('denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014', 'http://www.sciencedirect.com/science/article/pii/S1877050914013751', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Personalized Concept-driven Recommender System for Scientific Libraries [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.procs.2014.10.015\"\n     onclick=\"log_download('denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014', 'http://doi.org/10.1016/j.procs.2014.10.015', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('denart-tasso-apersonalizedconceptdrivenrecommendersystemforscientificlibraries-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{de_nart_personalized_2014,\n\ttitle = {A {Personalized} {Concept}-driven {Recommender} {System} for {Scientific} {Libraries}},\n\tvolume = {38},\n\tissn = {1877-0509},\n\turl = {http://www.sciencedirect.com/science/article/pii/S1877050914013751},\n\tdoi = {10.1016/j.procs.2014.10.015},\n\tabstract = {Recommender Systems can greatly enhance the exploitation of large digital\nlibraries; however, in order to achieve good accuracy with collaborative\nrecommenders some domain assumptions must be met, such as having a large\nnumber of users sharing similar interests over time. Such assumptions may\nnot hold in digital libraries, where users are structured in relatively\nsmall groups of experts whose interests may change in unpredictable ways:\nthis is the case of scientific and technical documents archives. Moreover,\nwhen recommending documents, users often expect insights on the\nrecommended content as well as a detailed explanation of why the system\nhas selected it, which cannot be provided by collaborative techniques. In\nthis paper we consider the domain of scientific publications repositories\nand propose a content-based recommender based upon a graph representation\nof concepts built up by linked keyphrases. This recommender is coupled\nwith a keyphrase extraction system able to generate meaningful metadata\nfor the documents, which are the basis for providing helpful and\nexplainable recommendations.},\n\turldate = {2015-09-23},\n\tjournal = {Procedia Comput. Sci.},\n\tauthor = {De Nart, D and Tasso, C},\n\tyear = {2014},\n\tpages = {84--91},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recommender Systems can greatly enhance the exploitation of large digital libraries; however, in order to achieve good accuracy with collaborative recommenders some domain assumptions must be met, such as having a large number of users sharing similar interests over time. Such assumptions may not hold in digital libraries, where users are structured in relatively small groups of experts whose interests may change in unpredictable ways: this is the case of scientific and technical documents archives. Moreover, when recommending documents, users often expect insights on the recommended content as well as a detailed explanation of why the system has selected it, which cannot be provided by collaborative techniques. In this paper we consider the domain of scientific publications repositories and propose a content-based recommender based upon a graph representation of concepts built up by linked keyphrases. This recommender is coupled with a keyphrase extraction system able to generate meaningful metadata for the documents, which are the basis for providing helpful and explainable recommendations.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2645710.2653363\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014', 'http://doi.acm.org/10.1145/2645710.2653363', event);\">\n    Improving Recommender Systems: User Roles and Lifecycles.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Nguyen, T. T\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '14</i>, pages 417–420, New York, NY, USA,  2014. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '14</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2645710.2653363\"\n     onclick=\"log_download('nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014', 'http://doi.acm.org/10.1145/2645710.2653363', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Improving Recommender Systems: User Roles and Lifecycles [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2645710.2653363\"\n     onclick=\"log_download('nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014', 'http://doi.org/10.1145/2645710.2653363', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('nguyen-improvingrecommendersystemsuserrolesandlifecycles-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{nguyen_improving_2014,\n\taddress = {New York, NY, USA},\n\ttitle = {Improving {Recommender} {Systems}: {User} {Roles} and {Lifecycles}},\n\turl = {http://doi.acm.org/10.1145/2645710.2653363},\n\tdoi = {10.1145/2645710.2653363},\n\tabstract = {In the era of big data, it is usually agreed that the more data we have,\nthe better results we can get. However, for some domains that heavily\ndepend on user inputs (such as recommender systems), the performance\nevaluation metrics are sensitive to the amount of noise introduced by\nusers. Such noise can be from users who only wanted to explore the\nsystems, and thus did not spend efforts to provide accurate inputs. Noise\ncan also be introduced by the methods of collecting user ratings. In my\ndissertation, I study how user data can affect prediction accuracies and\nperformances of recommendation algorithms. To that end, I investigate how\nthe data collection methods and the life cycles of users affect the\nprediction accuracies and the performance of recommendation algorithms.},\n\turldate = {2015-09-23},\n\tbooktitle = {{RecSys} &#x27;14},\n\tpublisher = {ACM},\n\tauthor = {Nguyen, Tien T},\n\tyear = {2014},\n\tnote = {Journal Abbreviation: RecSys &#x27;14},\n\tpages = {417--420},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In the era of big data, it is usually agreed that the more data we have, the better results we can get. However, for some domains that heavily depend on user inputs (such as recommender systems), the performance evaluation metrics are sensitive to the amount of noise introduced by users. Such noise can be from users who only wanted to explore the systems, and thus did not spend efforts to provide accurate inputs. Noise can also be introduced by the methods of collecting user ratings. In my dissertation, I study how user data can affect prediction accuracies and performances of recommendation algorithms. To that end, I investigate how the data collection methods and the life cycles of users affect the prediction accuracies and the performance of recommendation algorithms.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014', 'http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017', event);\">\n    Privacy-aware Location Privacy Preference Recommendations.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zhao, Y.; Ye, J.;  and Henderson, T.\n</span>\n\n  \n\n</span>\n\n  In <i>MOBIQUITOUS '14</i>, pages 120–129, ICST, Brussels, Belgium, Belgium,  2014. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)\n  <span class=\"note\">Journal Abbreviation: MOBIQUITOUS '14</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017\"\n     onclick=\"log_download('zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014', 'http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Privacy-aware Location Privacy Preference Recommendations [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.4108/icst.mobiquitous.2014.258017\"\n     onclick=\"log_download('zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014', 'http://doi.org/10.4108/icst.mobiquitous.2014.258017', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhao-ye-henderson-privacyawarelocationprivacypreferencerecommendations-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{zhao_privacy-aware_2014,\n\taddress = {ICST, Brussels, Belgium, Belgium},\n\ttitle = {Privacy-aware {Location} {Privacy} {Preference} {Recommendations}},\n\turl = {http://dx.doi.org/10.4108/icst.mobiquitous.2014.258017},\n\tdoi = {10.4108/icst.mobiquitous.2014.258017},\n\tabstract = {Location-Based Services have become increasingly popular due to the\nprevalence of smart devices and location-sharing applications such as\nFacebook and Foursquare. The protection of people&#x27;s sensitive location\ndata in such applications is an important requirement. Conventional\nlocation privacy protection methods, however, such as manually defining\nprivacy rules or asking users to make decisions each time they enter a new\nlocation may be overly complex, intrusive or unwieldy. An alternative is\nto use machine learning to predict people&#x27;s privacy preferences and\nautomatically configure settings. Model-based machine learning classifiers\nmay be too computationally complex to be used in real-world applications,\nor suffer from poor performance when training data are insufficient. In\nthis paper we propose a location-privacy recommender that can provide\npeople with recommendations of appropriate location privacy settings\nthrough user-user collaborative filtering. Using a real-world\nlocation-sharing dataset, we show that the prediction accuracy of our\nscheme (73.08\\%) is similar to the best performance of model-based\nclassifiers (75.30\\%), and at the same time causes fewer privacy leaks\n(11.75\\% vs 12.70\\%). Our scheme further outperforms model-based classifiers\nwhen there are insufficient training data. Since privacy preferences are\ninnately private, we make our recommender privacy-aware by obfuscating\npeople&#x27;s preferences. Our results show that obfuscation leads to a minimal\nloss of prediction accuracy (0.76\\%).},\n\turldate = {2015-09-23},\n\tbooktitle = {{MOBIQUITOUS} &#x27;14},\n\tpublisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},\n\tauthor = {Zhao, Yuchen and Ye, Juan and Henderson, Tristan},\n\tyear = {2014},\n\tnote = {Journal Abbreviation: MOBIQUITOUS &#x27;14},\n\tpages = {120--129},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people's sensitive location data in such applications is an important requirement. Conventional location privacy protection methods, however, such as manually defining privacy rules or asking users to make decisions each time they enter a new location may be overly complex, intrusive or unwieldy. An alternative is to use machine learning to predict people's privacy preferences and automatically configure settings. Model-based machine learning classifiers may be too computationally complex to be used in real-world applications, or suffer from poor performance when training data are insufficient. In this paper we propose a location-privacy recommender that can provide people with recommendations of appropriate location privacy settings through user-user collaborative filtering. Using a real-world location-sharing dataset, we show that the prediction accuracy of our scheme (73.08%) is similar to the best performance of model-based classifiers (75.30%), and at the same time causes fewer privacy leaks (11.75% vs 12.70%). Our scheme further outperforms model-based classifiers when there are insufficient training data. Since privacy preferences are innately private, we make our recommender privacy-aware by obfuscating people's preferences. Our results show that obfuscation leads to a minimal loss of prediction accuracy (0.76%).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1109/MIS.2014.23\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014', 'http://dx.doi.org/10.1109/MIS.2014.23', event);\">\n    Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Amolochitis, E; Christou, I T;  and Tan, Z.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Intell. Syst.</i>, 29(2): 92–96. March 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1109/MIS.2014.23\"\n     onclick=\"log_download('amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014', 'http://dx.doi.org/10.1109/MIS.2014.23', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Implementing a Commercial-Strength Parallel Hybrid Movie Recommendation Engine [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/MIS.2014.23\"\n     onclick=\"log_download('amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014', 'http://doi.org/10.1109/MIS.2014.23', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('amolochitis-christou-tan-implementingacommercialstrengthparallelhybridmovierecommendationengine-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{amolochitis_implementing_2014,\n\ttitle = {Implementing a {Commercial}-{Strength} {Parallel} {Hybrid} {Movie} {Recommendation} {Engine}},\n\tvolume = {29},\n\tissn = {1541-1672},\n\turl = {http://dx.doi.org/10.1109/MIS.2014.23},\n\tdoi = {10.1109/MIS.2014.23},\n\tabstract = {AMORE is a hybrid recommendation system that provides movie\nrecommendations for a major triple-play services provider in Greece.\nCombined with our own implementations of several user-, item-, and\ncontent-based recommendation algorithms, AMORE significantly outperforms\nother state-of-the-art implementations both in solution quality and\nresponse time. AMORE currently serves daily recommendation requests for\nall active subscribers of the provider&#x27;s video-on-demand services and has\ncontributed to an increase of rental profits and customer retention.},\n\tnumber = {2},\n\tjournal = {IEEE Intell. Syst.},\n\tauthor = {Amolochitis, E and Christou, I T and Tan, Zheng-Hua},\n\tmonth = mar,\n\tyear = {2014},\n\tpages = {92--96},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  AMORE is a hybrid recommendation system that provides movie recommendations for a major triple-play services provider in Greece. Combined with our own implementations of several user-, item-, and content-based recommendation algorithms, AMORE significantly outperforms other state-of-the-art implementations both in solution quality and response time. AMORE currently serves daily recommendation requests for all active subscribers of the provider's video-on-demand services and has contributed to an increase of rental profits and customer retention.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2507157.2507188\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013', 'http://doi.acm.org/10.1145/2507157.2507188', event);\">\n    Rating Support Interfaces to Improve User Experience and Recommender Accuracy.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Nguyen, T. T; Kluver, D.; Wang, T.; Hui, P.; Ekstrand, M. D; Willemsen, M. C;  and Riedl, J.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '13</i>, pages 149–156, New York, NY, USA,  2013. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '13</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2507157.2507188\"\n     onclick=\"log_download('nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013', 'http://doi.acm.org/10.1145/2507157.2507188', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Rating Support Interfaces to Improve User Experience and Recommender Accuracy [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2507157.2507188\"\n     onclick=\"log_download('nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013', 'http://doi.org/10.1145/2507157.2507188', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('nguyen-kluver-wang-hui-ekstrand-willemsen-riedl-ratingsupportinterfacestoimproveuserexperienceandrecommenderaccuracy-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{nguyen_rating_2013,\n\taddress = {New York, NY, USA},\n\ttitle = {Rating {Support} {Interfaces} to {Improve} {User} {Experience} and {Recommender} {Accuracy}},\n\turl = {http://doi.acm.org/10.1145/2507157.2507188},\n\tdoi = {10.1145/2507157.2507188},\n\tabstract = {One of the challenges for recommender systems is that users struggle to\naccurately map their internal preferences to external measures of quality\nsuch as ratings. We study two methods for supporting the mapping process:\n(i) reminding the user of characteristics of items by providing\npersonalized tags and (ii) relating rating decisions to prior rating\ndecisions using exemplars. In our study, we introduce interfaces that\nprovide these methods of support. We also present a set of methodologies\nto evaluate the efficacy of the new interfaces via a user experiment. Our\nresults suggest that presenting exemplars during the rating process helps\nusers rate more consistently, and increases the quality of the data.},\n\turldate = {2014-04-28},\n\tbooktitle = {{RecSys} &#x27;13},\n\tpublisher = {ACM},\n\tauthor = {Nguyen, Tien T and Kluver, Daniel and Wang, Ting-Yu and Hui, Pik-Mai and Ekstrand, Michael D and Willemsen, Martijn C and Riedl, John},\n\tyear = {2013},\n\tnote = {Journal Abbreviation: RecSys &#x27;13},\n\tpages = {149--156},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013', 'http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation', event);\">\n    Technical Report on evaluation of recommendations generated by spreading activation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Benjamin Heitmann, C. H.\n</span>\n\n  \n\n</span>\n\n  .  2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation\"\n     onclick=\"log_download('benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013', 'http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Technical Report on evaluation of recommendations generated by spreading activation [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('benjaminheitmann-technicalreportonevaluationofrecommendationsgeneratedbyspreadingactivation-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{benjamin_heitmann_technical_2013,\n\ttitle = {Technical {Report} on evaluation of recommendations generated by spreading activation},\n\turl = {http://www.researchgate.net/publication/237020679_Technical_Report_on_evaluation_of_recommendations_generated_by_spreading_activation},\n\tauthor = {Benjamin Heitmann, Conor Hayes},\n\tyear = {2013},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2012\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2012')\"\n      onkeypress=\"toggleGroup('group_2012')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2012</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2366002\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012', 'http://doi.acm.org/10.1145/2365952.2366002', event);\">\n    When recommenders fail: predicting recommender failure for algorithm selection and combination.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ekstrand, M. D;  and Riedl, J. T\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '12</i>, pages 233–236, New York, NY, USA,  2012. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '12</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2366002\"\n     onclick=\"log_download('ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012', 'http://doi.acm.org/10.1145/2365952.2366002', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"When recommenders fail: predicting recommender failure for algorithm selection and combination [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2365952.2366002\"\n     onclick=\"log_download('ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012', 'http://doi.org/10.1145/2365952.2366002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ekstrand-riedl-whenrecommendersfailpredictingrecommenderfailureforalgorithmselectionandcombination-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ekstrand_when_2012,\n\taddress = {New York, NY, USA},\n\ttitle = {When recommenders fail: predicting recommender failure for algorithm selection and combination},\n\turl = {http://doi.acm.org/10.1145/2365952.2366002},\n\tdoi = {10.1145/2365952.2366002},\n\tabstract = {Hybrid recommender systems --- systems using multiple algorithms together\nto improve recommendation quality --- have been well-known for many years\nand have shown good performance in recent demonstrations such as the\nNetFlix Prize. Modern hybridization techniques, such as feature-weighted\nlinear stacking, take advantage of the hypothesis that the relative\nperformance of recommenders varies by circumstance and attempt to optimize\neach item score to maximize the strengths of the component recommenders.\nLess attention, however, has been paid to understanding what these\nstrengths and failure modes are. Understanding what causes particular\nrecommenders to fail will facilitate better selection of the component\nrecommenders for future hybrid systems and a better understanding of how\nindividual recommender personalities can be harnessed to improve the\nrecommender user experience. We present an analysis of the predictions\nmade by several well-known recommender algorithms on the MovieLens 10M\ndata set, showing that for many cases in which one algorithm fails, there\nis another that will correctly predict the rating.},\n\turldate = {2012-12-13},\n\tbooktitle = {{RecSys} &#x27;12},\n\tpublisher = {ACM},\n\tauthor = {Ekstrand, Michael D and Riedl, John T},\n\tyear = {2012},\n\tnote = {Journal Abbreviation: RecSys &#x27;12},\n\tpages = {233--236},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Hybrid recommender systems — systems using multiple algorithms together to improve recommendation quality — have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the relative performance of recommenders varies by circumstance and attempt to optimize each item score to maximize the strengths of the component recommenders. Less attention, however, has been paid to understanding what these strengths and failure modes are. Understanding what causes particular recommenders to fail will facilitate better selection of the component recommenders for future hybrid systems and a better understanding of how individual recommender personalities can be harnessed to improve the recommender user experience. We present an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2365974\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012', 'http://doi.acm.org/10.1145/2365952.2365974', event);\">\n    How many bits per rating?.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kluver, D.; Nguyen, T. T; Ekstrand, M.; Sen, S.;  and Riedl, J.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '12</i>, pages 99–106, New York, NY, USA,  2012. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '12</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2365974\"\n     onclick=\"log_download('kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012', 'http://doi.acm.org/10.1145/2365952.2365974', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"How many bits per rating? [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2365952.2365974\"\n     onclick=\"log_download('kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012', 'http://doi.org/10.1145/2365952.2365974', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kluver-nguyen-ekstrand-sen-riedl-howmanybitsperrating-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{kluver_how_2012,\n\taddress = {New York, NY, USA},\n\ttitle = {How many bits per rating?},\n\turl = {http://doi.acm.org/10.1145/2365952.2365974},\n\tdoi = {10.1145/2365952.2365974},\n\tabstract = {Most recommender systems assume user ratings accurately represent user\npreferences. However, prior research shows that user ratings are imperfect\nand noisy. Moreover, this noise limits the measurable predictive power of\nany recommender system. We propose an information theoretic framework for\nquantifying the preference information contained in ratings and\npredictions. We computationally explore the properties of our model and\napply our framework to estimate the efficiency of different rating scales\nfor real world datasets. We then estimate how the amount of information\npredictions give to users is related to the scale ratings are collected\non. Our findings suggest a tradeoff in rating scale granularity: while\nprevious research indicates that coarse scales (such as thumbs up / thumbs\ndown) take less time, we find that ratings with these scales provide less\npredictive value to users. We introduce a new measure, preference bits per\nsecond, to quantitatively reconcile this tradeoff.},\n\turldate = {2013-09-12},\n\tbooktitle = {{RecSys} &#x27;12},\n\tpublisher = {ACM},\n\tauthor = {Kluver, Daniel and Nguyen, Tien T and Ekstrand, Michael and Sen, Shilad and Riedl, John},\n\tyear = {2012},\n\tnote = {Journal Abbreviation: RecSys &#x27;12},\n\tpages = {99--106},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2365984\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012', 'http://doi.acm.org/10.1145/2365952.2365984', event);\">\n    Scalable Similarity-based Neighborhood Methods with MapReduce.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Schelter, S.; Boden, C.;  and Markl, V.\n</span>\n\n  \n\n</span>\n\n  In <i>RecSys '12</i>, pages 163–170, New York, NY, USA,  2012. ACM\n  <span class=\"note\">Journal Abbreviation: RecSys '12</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://doi.acm.org/10.1145/2365952.2365984\"\n     onclick=\"log_download('schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012', 'http://doi.acm.org/10.1145/2365952.2365984', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scalable Similarity-based Neighborhood Methods with MapReduce [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/2365952.2365984\"\n     onclick=\"log_download('schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012', 'http://doi.org/10.1145/2365952.2365984', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('schelter-boden-markl-scalablesimilaritybasedneighborhoodmethodswithmapreduce-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{schelter_scalable_2012,\n\taddress = {New York, NY, USA},\n\ttitle = {Scalable {Similarity}-based {Neighborhood} {Methods} with {MapReduce}},\n\turl = {http://doi.acm.org/10.1145/2365952.2365984},\n\tdoi = {10.1145/2365952.2365984},\n\tabstract = {Similarity-based neighborhood methods, a simple and popular approach to\ncollaborative filtering, infer their predictions by finding users with\nsimilar taste or items that have been similarly rated. If the number of\nusers grows to millions, the standard approach of sequentially examining\neach item and looking at all interacting users does not scale. To solve\nthis problem, we develop a MapReduce algorithm for the pairwise item\ncomparison and top-N recommendation problem that scales linearly with\nrespect to a growing number of users. This parallel algorithm is able to\nwork on partitioned data and is general in that it supports a wide range\nof similarity measures. We evaluate our algorithm on a large dataset\nconsisting of 700 million song ratings from Yahoo! Music.},\n\turldate = {2015-09-23},\n\tbooktitle = {{RecSys} &#x27;12},\n\tpublisher = {ACM},\n\tauthor = {Schelter, Sebastian and Boden, Christoph and Markl, Volker},\n\tyear = {2012},\n\tnote = {Journal Abbreviation: RecSys &#x27;12},\n\tpages = {163--170},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Similarity-based neighborhood methods, a simple and popular approach to collaborative filtering, infer their predictions by finding users with similar taste or items that have been similarly rated. If the number of users grows to millions, the standard approach of sequentially examining each item and looking at all interacting users does not scale. To solve this problem, we develop a MapReduce algorithm for the pairwise item comparison and top-N recommendation problem that scales linearly with respect to a growing number of users. This parallel algorithm is able to work on partitioned data and is general in that it supports a wide range of similarity measures. We evaluate our algorithm on a large dataset consisting of 700 million song ratings from Yahoo! Music.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1371/journal.pone.0044620\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012', 'http://dx.doi.org/10.1371/journal.pone.0044620', event);\">\n    Predicting Human Preferences Using the Block Structure of Complex Social Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Guimerà, R.; Llorente, A.; Moro, E.;  and Sales-Pardo, M.\n</span>\n\n  \n\n</span>\n\n  <i>PLoS One</i>, 7(9): e44620. September 2012.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1371/journal.pone.0044620\"\n     onclick=\"log_download('guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012', 'http://dx.doi.org/10.1371/journal.pone.0044620', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Predicting Human Preferences Using the Block Structure of Complex Social Networks [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1371/journal.pone.0044620\"\n     onclick=\"log_download('guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012', 'http://doi.org/10.1371/journal.pone.0044620', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('guimer-llorente-moro-salespardo-predictinghumanpreferencesusingtheblockstructureofcomplexsocialnetworks-2012')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{guimera_predicting_2012,\n\ttitle = {Predicting {Human} {Preferences} {Using} the {Block} {Structure} of {Complex} {Social} {Networks}},\n\tvolume = {7},\n\turl = {http://dx.doi.org/10.1371/journal.pone.0044620},\n\tdoi = {10.1371/journal.pone.0044620},\n\tabstract = {With ever-increasing available data, predicting individuals&#x27; preferences\nand helping them locate the most relevant information has become a\npressing need. Understanding and predicting preferences is also important\nfrom a fundamental point of view, as part of what has been called a “new”\ncomputational social science. Here, we propose a novel approach based on\nstochastic block models, which have been developed by sociologists as\nplausible models of complex networks of social interactions. Our model is\nin the spirit of predicting individuals&#x27; preferences based on the\npreferences of others but, rather than fitting a particular model, we rely\non a Bayesian approach that samples over the ensemble of all possible\nmodels. We show that our approach is considerably more accurate than\nleading recommender algorithms, with major relative improvements between\n38\\% and 99\\% over industry-level algorithms. Besides, our approach sheds\nlight on decision-making processes by identifying groups of individuals\nthat have consistently similar preferences, and enabling the analysis of\nthe characteristics of those groups.},\n\tnumber = {9},\n\turldate = {2014-10-04},\n\tjournal = {PLoS One},\n\tauthor = {Guimerà, Roger and Llorente, Alejandro and Moro, Esteban and Sales-Pardo, Marta},\n\tmonth = sep,\n\tyear = {2012},\n\tpages = {e44620},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);