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dc.contributor.authorYu, Yonghong
dc.contributor.authorWang, Can
dc.contributor.authorWang, Hao
dc.contributor.authorGao, Yang
dc.date.accessioned2021-02-01T05:35:34Z
dc.date.available2021-02-01T05:35:34Z
dc.date.issued2017
dc.identifier.issn0924-669Xen_US
dc.identifier.doi10.1007/s10489-016-0841-8en_US
dc.identifier.urihttp://hdl.handle.net/10072/401613
dc.description.abstractRecommender systems have attracted lots of attention since they alleviate the information overload problem for users. Matrix factorization is one of the most widely employed collaborative filtering techniques in the research of recommender systems due to its effectiveness and efficiency in dealing with very large user-item rating matrices. Recently, additional information, such as social network and user demographics, have been adopted by several recommendation algorithms to provide useful insights for matrix factorization techniques. However, most of them focus on dealing with the cold start user problem and ignore the cold start item problem. In addition, there are few suitable similarity measures for these content enhanced matrix factorization approaches to compute the similarity between categorical items. In this paper, we propose an attributes coupling based matrix factorization method by incorporating item-attribute information into the matrix factorization model as well as adopting coupled object similarity to capture the relationship among items. Item-attribute information is formed as an item relationship regularization term to constrain the process of matrix factorization. Experimental results on two real data sets show that our proposed method outperforms the state-of-the-art recommendation algorithms and can effectively cope with the cold start item problem when such item-attribute information is available.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.relation.ispartofpagefrom521en_US
dc.relation.ispartofpageto533en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalApplied Intelligenceen_US
dc.relation.ispartofvolume46en_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processingen_US
dc.subject.fieldofresearchcode0801en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsComputer Scienceen_US
dc.subject.keywordsRecommender systemsen_US
dc.titleAttributes coupling based matrix factorization for item recommendationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationYu, Y; Wang, C; Wang, H; Gao, Y, Attributes coupling based matrix factorization for item recommendation, Applied Intelligence, 2017, 46 (3), pp. 521-533en_US
dc.date.updated2021-02-01T05:33:39Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can


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