Abstract: It has become the main task of mobile recommender systems to further improve the prediction quality and solve the data sparsity and cold-start problems that may exist by employing mobile context and mobile social network information etc.We combined users,services and users’ social network information for recommendation to alleviate the data sparsity and cold-start problems by using the factor analysis method based on matrix factorization (MF).In order to increase the trust matrix density,in this paper we imported the indirect trust relationship,and then proposed a trust relationship calculation method which only use the mobile social network information to build trust matrix to reduce the user’s active identification for trust relationship.And the trust calculation method is in line with the characteristics of mobile social network.The experimental results show that the introduction of the indirect trust relationship can improve the prediction accuracy,and our method outperforms some existing MF methods and traditional collaborative filtering algorithm in the aspect of accuracy,especially in the circumstance that users have made very few ratings or even none at all.

Key words: Mobile recommendation,Social recommendation,Matrix factorization,Trust,Data sparsity