A synthetic approach for recommendation: Combining ratings, social relations, and reviews

Guang Neng Hu, Xin Yu Dai, Celine SONG, Shu Jian Huang, Jia Jun Chen

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

35 Citations (Scopus)

Abstract

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social MF) can integrate ratings with social relations and topic matrix factorization can integrate ratings with item reviews, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the two approaches, in two steps. First, we extend Social MF to exploit the graph structure of neighbors. Second, we propose a novel framework MR3 to jointly model these three types of information effectively for rating prediction by aligning latent factors and hidden topics. We achieve more accurate rating prediction on two real-life datasets. Furthermore, we measure the contribution of each data source to the proposed framework.

Original languageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherAAAI press
Pages1756-1762
Number of pages7
ISBN (Print)9781577357384
Publication statusPublished - Jul 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina, Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Conference

Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Country/TerritoryArgentina
CityBuenos Aires
Period25/07/1531/07/15

Scopus Subject Areas

  • Artificial Intelligence

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