NeuRec: On nonlinear transformation for personalized ranking

Zhang, S; Yao, L; Sun, A; Wang, S; Long, G; Dong, M

NeuRec: On nonlinear transformation for personalized ranking

Zhang, S Yao, L Sun, A Wang, S Long, G

Dong, M

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2018, 2018-July pp. 3669 - 3675
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Yao, L	en_US
dc.contributor.author	Sun, A	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515	en_US
dc.contributor.author	Dong, M	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2018, 2018-July pp. 3669 - 3675	en_US
dc.identifier.isbn	9780999241127	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131570
dc.description.abstract	© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: userbased NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	NeuRec: On nonlinear transformation for personalized ranking	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: userbased NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

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http://hdl.handle.net/10453/131570