Mining Heterogeneous Influence and Indirect Trust for Recommendation

Relationships between users in social networks have been widely used to improve recommender systems. However, actual social relationships are always sparse, which sometimes bring great harm to the performance of recommender systems. In fact, a user may interact with others that he/she does not connect directly, and thus has an impact on these users. To mine abundant information for social recommendation and alleviate the problem of data sparsity, we study the process of trust propagation and propose a novel recommendation algorithm that incorporates multiple information sources into matrix factorization. We first explore heterogeneous influence strength for each pair of linked users and mine indirect trust between users by using trust propagation and aggregation strategy in social networks. Then, explicit and implicit information of user trust and ratings are incorporated into matrix factorization, and the influence of indirect trust is considered in the recommendation process. Experimental results show that the proposed model achieves better performance than some state-of-the-art recommendation models in terms of accuracy and relieves the cold-start problem.