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

{"_id":"vCFh4GXjDGi4c8KAi","bibbaseid":"asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019","authorIDs":[],"author_short":["Asenova, M.","Chrysoulas, C."],"bibdata":{"bibtype":"article","type":"article","title":"Personalized Micro-Service Recommendation System for Online News","volume":"160","issn":"1877-0509","url":"http://www.sciencedirect.com/science/article/pii/S1877050919317399","doi":"10.1016/j.procs.2019.11.039","abstract":"In the era of artificial intelligence and high technology advance our life is dependent on them in every aspect. The dynamic environment forces us to plan our time with conscious and every minute is valuable. To help individuals and corporations see information that is only relevant to them, recommendation systems are in place. Popular platforms that such as Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation systems to better serve the needed of their users. This research paper gives insight of building a microservice recommendation system for online news. Research in recommendation systems is mainly focused on improving user’s experience based mainly on personalization information, such as preferences, and searching history. To determine the initial preferences of a user an initial menu of topics/themes is provided for the user to choose from. In order to reflect as precise as possible the searching interests regarding news of user, all of his interactions are thoroughly recorded and in depth analyzed, based on advanced machine learning techniques, when adjusting the news topics, the user is interested for. Based on the aforementioned approach, a personalized recommendation system for online news has been developed. Existing techniques has been researched and evaluated to aid the decision about picking the best approach for the software to be implemented. Frameworks/technologies used for the development are Java 8, Spring boot, Spring MVC, Maven and MongoDB.","journal":"Procedia Comput. Sci.","author":[{"propositions":[],"lastnames":["Asenova"],"firstnames":["Marchela"],"suffixes":[]},{"propositions":[],"lastnames":["Chrysoulas"],"firstnames":["Christos"],"suffixes":[]}],"month":"January","year":"2019","keywords":"TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases","pages":"610–615","bibtex":"@article{asenova_personalized_2019,\n\ttitle = {Personalized {Micro}-{Service} {Recommendation} {System} for {Online} {News}},\n\tvolume = {160},\n\tissn = {1877-0509},\n\turl = {http://www.sciencedirect.com/science/article/pii/S1877050919317399},\n\tdoi = {10.1016/j.procs.2019.11.039},\n\tabstract = {In the era of artificial intelligence and high technology advance our life\nis dependent on them in every aspect. The dynamic environment forces us to\nplan our time with conscious and every minute is valuable. To help\nindividuals and corporations see information that is only relevant to\nthem, recommendation systems are in place. Popular platforms that such as\nAmazon, Ebay, Netflix, YouTube, make use of advanced recommendation\nsystems to better serve the needed of their users. This research paper\ngives insight of building a microservice recommendation system for online\nnews. Research in recommendation systems is mainly focused on improving\nuser’s experience based mainly on personalization information, such as\npreferences, and searching history. To determine the initial preferences\nof a user an initial menu of topics/themes is provided for the user to\nchoose from. In order to reflect as precise as possible the searching\ninterests regarding news of user, all of his interactions are thoroughly\nrecorded and in depth analyzed, based on advanced machine learning\ntechniques, when adjusting the news topics, the user is interested for.\nBased on the aforementioned approach, a personalized recommendation system\nfor online news has been developed. Existing techniques has been\nresearched and evaluated to aid the decision about picking the best\napproach for the software to be implemented. Frameworks/technologies used\nfor the development are Java 8, Spring boot, Spring MVC, Maven and\nMongoDB.},\n\tjournal = {Procedia Comput. Sci.},\n\tauthor = {Asenova, Marchela and Chrysoulas, Christos},\n\tmonth = jan,\n\tyear = {2019},\n\tkeywords = {TF-IDF, collaborative filtering, cosine similarity, recommendation engine, recommendation phases},\n\tpages = {610--615},\n}\n\n","author_short":["Asenova, M.","Chrysoulas, C."],"key":"asenova_personalized_2019","id":"asenova_personalized_2019","bibbaseid":"asenova-chrysoulas-personalizedmicroservicerecommendationsystemforonlinenews-2019","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S1877050919317399"},"keyword":["TF-IDF","collaborative filtering","cosine similarity","recommendation engine","recommendation phases"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://api.zotero.org/users/6655/collections/TJPPJ92X/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100","creationDate":"2020-05-26T10:28:30.534Z","downloads":0,"keywords":["tf-idf","collaborative filtering","cosine similarity","recommendation engine","recommendation phases"],"search_terms":["personalized","micro","service","recommendation","system","online","news","asenova","chrysoulas"],"title":"Personalized Micro-Service Recommendation System for Online News","year":2019,"dataSources":["5Dp4QphkvpvNA33zi","jfoasiDDpStqkkoZB","BiuuFc45aHCgJqDLY"]}