An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
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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.