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 language | English |
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Title of host publication | IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence |
Editors | Michael Wooldridge, Qiang Yang |
Publisher | AAAI press |
Pages | 1756-1762 |
Number of pages | 7 |
ISBN (Print) | 9781577357384 |
Publication status | Published - Jul 2015 |
Event | 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina, Buenos Aires, Argentina Duration: 25 Jul 2015 → 31 Jul 2015 https://ijcai-15.org/ https://www.ijcai.org/proceedings/2015 |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Volume | 2015-January |
ISSN (Print) | 1045-0823 |
Conference
Conference | 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 |
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Country/Territory | Argentina |
City | Buenos Aires |
Period | 25/07/15 → 31/07/15 |
Internet address |
Scopus Subject Areas
- Artificial Intelligence