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dc.contributor.authorZhang, S
dc.contributor.authorYao, L
dc.contributor.authorXu, X
dc.contributor.authorWang, S
dc.contributor.authorZhu, L
dc.date.accessioned2018-03-05T01:39:20Z
dc.date.available2018-03-05T01:39:20Z
dc.date.issued2017
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-70087-8_20
dc.identifier.urihttp://hdl.handle.net/10072/370473
dc.description.abstractIn this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom185
dc.relation.ispartofpageto193
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume10634
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode089999
dc.titleHybrid Collaborative Recommendation via Semi-AutoEncoder
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Sen


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