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dc.contributor.authorDu, Liangen_US
dc.contributor.authorLi, Xuanen_US
dc.contributor.authorShen, Yi-Dongen_US
dc.date.accessioned2017-05-03T11:59:26Z
dc.date.available2017-05-03T11:59:26Z
dc.date.issued2011en_US
dc.date.modified2012-09-21T04:15:59Z
dc.identifier.issn03029743en_US
dc.identifier.doi10.1007/978-3-642-25856-5_28en_US
dc.identifier.urihttp://hdl.handle.net/10072/46984
dc.description.abstractItem recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.publisher.placeGermanyen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom372en_US
dc.relation.ispartofpageto385en_US
dc.relation.ispartofjournalLecture Notes in Computer scienceen_US
dc.relation.ispartofvolume7121en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleUser Graph Regularized Pairwise Matrix Factorization for Item Recommendationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2011
gro.hasfulltextNo Full Text
gro.griffith.authorShen, Yi-Dong


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