Combining Nonnegative Matrix Factorization technique with Trust Relationships for Recommendation in Social Networks

Recommender systems has shown as effective tools that are proposed for helping users to select their interested items. Collaborative filtering is a well-known and frequently used recommender system applied successfully in many e-commerce websites. However, these systems have poor performance while facing cold-start users (items). To address such issues, in this paper, a social regularization method is proposed which combines the social network information of users in a nonnegative matrix factorization framework. The proposed method integrates multiple information sources such as user-item ratings and trust statements to reduce the cold-start and data sparsity issues. Moreover, the alternating direction method is used to improve the convergence speed and reduce the computational cost. We use two well-known datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation methods for recommendation in social networks.