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Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

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dc.contributor.authorAdomavicius, Gediminas-
dc.contributor.authorKwon, YoungOk-
dc.date.available2021-02-22T12:46:34Z-
dc.date.issued2012-05-
dc.identifier.issn1041-4347-
dc.identifier.issn1558-2191-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/11919-
dc.description.abstractRecommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.-
dc.format.extent16-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE COMPUTER SOC-
dc.titleImproving Aggregate Recommendation Diversity Using Ranking-Based Techniques-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/TKDE.2011.15-
dc.identifier.scopusid2-s2.0-84859702336-
dc.identifier.wosid000301746800010-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.24, no.5, pp 896 - 911-
dc.citation.titleIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING-
dc.citation.volume24-
dc.citation.number5-
dc.citation.startPage896-
dc.citation.endPage911-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordPlusSINGULAR VALUE DECOMPOSITION-
dc.subject.keywordPlusLEAST-SQUARES-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordAuthorRecommender systems-
dc.subject.keywordAuthorrecommendation diversity-
dc.subject.keywordAuthorranking functions-
dc.subject.keywordAuthorperformance evaluation metrics-
dc.subject.keywordAuthorcollaborative filtering-
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