Abstract:

To address the inherent data sparsity and coldstart problem in the recommender system, additional sources of information about users or items are usually adopted. We propose a novel matrix factorization based recommendation algorithm which integrates the indirect influence of other users on active user’s future ratings. Furthermore, we incorporate the trust relation in social network into our algorithm. In addition, we introduce a weighted regularization factor to avoid overfitting when learning parameters. We conduct experiments on Epinions dataset and Ciao dataset in normal cases and coldstart cases. Experimental results demonstrate that the recommendation accuracy of our algorithm is greatly improved in comparison with other counterparts and it can better handle the problems concerned.

Key words: recommender system, social network, matrix factorization, trust relation, indirect influence