Social recommendation with an essential preference space

Liu, CY; Zhou, C; Wu, J; Hu, Y; Guo, L

Social recommendation with an essential preference space

Liu, CY Zhou, C Wu, J Hu, Y Guo, L

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Publication Type:: Conference Proceeding
Citation:: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018, pp. 346 - 353
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Liu, CY	en_US
dc.contributor.author	Zhou, C	en_US
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Hu, Y	en_US
dc.contributor.author	Guo, L	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2018, pp. 346 - 353	en_US
dc.identifier.isbn	9781577358008	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133210
dc.description.abstract	Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are based on the tractable assumption that users consider the same factors to make decisions in both recommender systems and social networks. However, this assumption is not in concert with real-world situations, since users usually show different preferences in different scenarios. In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation. In particular, we assume that the user preferences in different scenarios are results of different linear combinations from a more underlying user preference space. Based on this assumption, we propose a novel social recommendation framework, called social recommendation with an essential preferences space (SREPS), which simultaneously models the structural information in the social network, the rating and the consumption information in the recommender system under the capture of essential preference space. Experimental results on four real-world datasets demonstrate the superiority of the proposed SREPS model compared with seven state-of-the-art social recommendation methods.	en_US
dc.relation.ispartof	32nd AAAI Conference on Artificial Intelligence, AAAI 2018	en_US
dc.title	Social recommendation with an essential preference space	en_US
dc.type	Conference Proceeding
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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are based on the tractable assumption that users consider the same factors to make decisions in both recommender systems and social networks. However, this assumption is not in concert with real-world situations, since users usually show different preferences in different scenarios. In this paper, we investigate how to exploit the differences between user preference in recommender systems and that in social networks, with the aim to further improve the social recommendation. In particular, we assume that the user preferences in different scenarios are results of different linear combinations from a more underlying user preference space. Based on this assumption, we propose a novel social recommendation framework, called social recommendation with an essential preferences space (SREPS), which simultaneously models the structural information in the social network, the rating and the consumption information in the recommender system under the capture of essential preference space. Experimental results on four real-world datasets demonstrate the superiority of the proposed SREPS model compared with seven state-of-the-art social recommendation methods.

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http://hdl.handle.net/10453/133210