Abstract
Collaborative filtering suffers from the problems of data spar-sity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TntstSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.
Original language | English |
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Title of host publication | Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 |
Publisher | AI Access Foundation |
Pages | 123-129 |
Number of pages | 7 |
Volume | 1 |
ISBN (Electronic) | 9781577356998 |
Publication status | Published - 1 Jun 2015 |
Externally published | Yes |
Event | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States Duration: 25 Jan 2015 → 30 Jan 2015 |
Conference
Conference | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 |
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Country/Territory | United States |
City | Austin |
Period | 25/01/15 → 30/01/15 |