Hybrid collaborative recommendation via Semi-AutoEncoder

Zhang, S; Yao, L; Xu, X; Wang, S; Zhu, L

Hybrid collaborative recommendation via Semi-AutoEncoder

Zhang, S Yao, L Xu, X Wang, S Zhu, L

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10634 LNCS pp. 185 - 193
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Yao, L	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Zhu, L	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10634 LNCS pp. 185 - 193	en_US
dc.identifier.isbn	9783319700861	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127360
dc.description.abstract	© Springer International Publishing AG 2017. In 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.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-70087-8_20	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Hybrid collaborative recommendation via Semi-AutoEncoder	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10634 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10634 LNCS	en_US

Abstract:

© Springer International Publishing AG 2017. In 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.

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http://hdl.handle.net/10453/127360