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SIGIR
2009
ACM

Learning to recommend with social trust ensemble

14 years 28 days ago
Learning to recommend with social trust ensemble
As an indispensable technique in the field of Information Filtering, Recommender System has been well studied and developed both in academia and in industry recently. However, most of current recommender systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot easily deal with users who have made very few ratings. (2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real world recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework, which naturally fuses the users’ tastes and their trusted friends’ favors together. In this framework, we coin the term Social Trust Ensemble to represent the formulation of the ...
Hao Ma, Irwin King, Michael R. Lyu
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where SIGIR
Authors Hao Ma, Irwin King, Michael R. Lyu
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