Integrating User Social Status and Matrix Factorization for Item Recommendation

With the increasing popularity of online social network services, social networks platforms provide rich information for recommender systems. Based on the assumption that friends share more common interests than non-friends and users tend to accept the item recommendations from friends, more and more recommender systems utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: 1) in different domains, users tend to trust different friends; 2) the degree of influence that a user is affected by their trusted friends is different in different domains since the user has different social status in different domains. In this paper, we first infer domain-specific social trust relation networks based on original users’ rating information and social network information, and then compute each user’s social status by leveraging PageRank algorithm for each specific domain. Finally, we propose a novel recommendation algorithm by integrating users’ social status with matrix factorization model. Experimental results on real-world dataset show that our proposed approach outperforms traditional social-network-based recommenda-tion algorithms.