Abstract
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 Personal factors with Weighted Social 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.
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Wang, Z., Yang, Y., Hu, Q., He, L. (2015). An Empirical Study of Personal Factors and Social Effects on Rating Prediction. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_58
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DOI: https://doi.org/10.1007/978-3-319-18038-0_58
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