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\n@unpublished{aridor_economics_2022,\n\ttitle = {The {Economics} of {Recommender} {Systems}: {Evidence} from a {Field} {Experiment} on {MovieLens}},\n\turl =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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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 = {Proceedings of the {Eighth} {ACM} {Conference} on {Recommender} {Systems}},\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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n
@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\n