Utilizing Social and Behavioral Neighbors for Personalized Recommendation

Abstract

As a successful and effective technique, recommendation systems have been widely studied. Recently, with the popularity of social networks, some researchers have proposed the social recommendation, which considers the social relations between users besides the rating data. However, in real world scenarios, both the social relations and ratings are very sparse, how to combine them together to improve the performance becomes a critical issue. To that end, in this paper, we propose a unified three-stage recommendation framework named Random Walk Neighborhood-aware Matrix Factorization(RWNMF), which can effectively integrate the social and rating data together and alleviate the sparsity problem. Specifically, we first perform random walk on social graph to find potential neighbors of each user, then select behavioral neighbors based on the rating data. Lastly, both the social neighbors and behavioral neighbors can be incorporated into traditional SocialMF, leading to more accurate recommendations. Experimental results on Epinions and Flixster datasets demonstrate our approach outperforms the state-of-the-art algorithms.