Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks

Pengfei Li, Hua Lu, Gang Zheng, Qian Zheng, Long Yang, Gang Pan

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review


Many e-commerce platforms today allow users to give their rating scores and reviews on items as well as to establish social relationships with other users. As a result, such platforms accumulate heterogeneous data including numeric scores, short textual reviews, and social relationships. However, many recommender systems only consider historical user feedbacks in modeling user preferences. More specifically, most existing recommendation approaches only use rating scores but ignore reviews and social relationships in the user-generated data. In this paper, we propose TSNPF-a latent factor model to effectively capture user preferences and item features. Employing Poisson factorization, TSNPF fully exploits the wealth of information in rating scores, review text and social relationships altogether. It extracts topics of items and users from the review text and makes use of similarities between user pairs with social relationships, which results in a comprehensive understanding of user preferences. Experimental results on real-world datasets demonstrate that our TSNPF approach is highly effective at recommending items to users.
TitelWWW '19 The World Wide Web Conference : Proceedings of The World Wide Web Conference WWW 2019
RedaktørerLing Liu, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo Baeza-Yates, Leila Zia
Antal sider11
UdgivelsesstedNew York
ForlagAssociation for Computing Machinery
ISBN (Trykt)9781450366748
StatusUdgivet - 2019
Udgivet eksterntJa
BegivenhedThe World Wide Web Conference 2019 - San Francisco, USA
Varighed: 13 maj 201917 maj 2019


KonferenceThe World Wide Web Conference 2019
BySan Francisco

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