Incorporating Social Network and User’s Preference in Matrix Factorization for Recommendation.

Recommender systems have been comprehensively applied in many industries, such as social Web sites, e-commerce, tourism service and so on, although suffering from the data sparsity and cold start problems. Currently, due to the advantage of online social networking, many social network-based recommendation scenarios have been developed to improve the recommendation accuracy, via exploring hidden social relations between users from the social network. In this article, focusing on addressing these problems, a novel social network and preference-based recommendation method—SRMP, is proposed, which incorporates the social network information and user’s preference in matrix factorization for recommendation. In contrast to previous approaches, to improve the recommendation accuracy, SRMP performs recommendation in each independent sub-community, which is derived from the initial social community according to different category tags. The experimental analysis on large real-world datasets demonstrates that the proposed method SRMP outperforms state-of-the-art approaches, especially in recommendation accuracy and solving the cold start problem. [ABSTRACT FROM AUTHOR]