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Erschienen in:
2015 | OriginalPaper | Buchkapitel
An Empirical Study of Personal Factors and Social Effects on Rating Prediction
verfasst von : Zhijin Wang, Yan Yang, Qinmin Hu, Liang He
Erschienen in: Advances in Knowledge Discovery and Data Mining
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In social networks, the link between a pair of friends has been reported effective in improving recommendation accuracy. Previous studies mainly based on the assumption that any pair of friends shall have similar interests, via minimizing the gap between user’s taste and the average (or similar) taste of this user’s friends to reduce the error of rating prediction. However, these methods ignore the diversity of user’s taste. In this paper, we focus on learning the diversity of user’s taste and effects from this user’s friends in terms of rating behavior. We propose a novel recommendation approach, namely
P
ersonal factors with
W
eighted
S
ocial effects Matrix Factorization (PWS), which utilities both user’s taste and social effects to provide recommendations. Experimental results carried out on 3 datasets, show the effectiveness of the proposed approach.