Matrix factorization has shown as an effective collaborative filtering approach to build a successful recommender system. However, these systems have poor performance while facing cold-start users (items). To tackle this issue, in this paper, a social regularization method called TrustANLF 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. To evaluate the proposed method, several experiments are performed on two real-world datasets. The results report the effectiveness of the proposed method compared to several stateof-the-art methods.
