User Interests Imbalance Exploration in Social Recommendation: A Fitness Adaptation

Recent years have witnessed an increasing interest in how to incorporate social network information into recommendation algorithms to enhance the user experience. In this paper, we find the phenomenon that users in the contexts of recommendation system and social network do not share the same interest space. Based on this finding, we proposed the social regulatory factor regression model (SRFRM) which could connect different interest spaces in different contexts together in an unified latent factor model. Specifically, different from the traditional social based latent factor models with strong limitation that all sides share the same feature space, the proposed method leverages the regulatory factor number on both sides to meet the fact that users and items or users in different contexts may not share the same interest space. It works by incorporating two linear transformation matrices into the matrix co-factorization framework that matrix factorization of user ratings is regularized by that of social trust network. We study a large subsets of data from epinions.com and douban.com respectively. The experimental results indicate that users in different contexts have different interest spaces and our model achieves a higher performance compared with related state-of-the-art methods.