Recommender System Based on Social Trust Relationships

The development of social network has increased the importance of social recommendation. However, social recommender systems have only recently been given research attention. Social relationships between users, especially trust relationships, can facilitate the design of social recommender systems. Such systems are based on the idea that users linked by a social network tend to share similar interests. Existing recommender approaches based on social trust relationships do not fully utilize such relationships and thus have low prediction accuracy or slow convergence speed. We propose a factor analysis approach that explicitly and implicitly uses social trust relationships simultaneously to overcome this limitation and fully utilize social trust relationships. Our approach combines the advantages of the existing two approaches, social recommendation using probabilistic matrix factorization and learning to recommend with social trust ensemble. Based on Epinions data sets, our approach has both significantly higher prediction accuracy and convergence speed than traditional collaborative filtering technology and state-of-the-art trust-based recommendation approaches.