SoRec: social recommendation using probabilistic matrix factorization

Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users who have made very few ratings or even none at all. Moreover, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the social interactions or connections among users. In view of the exponential growth of information generated by online social networks, social network analysis is becoming important for many Web applications. Following the intuition that a person's social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social net...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King
• Keywords: CIKM 2008, Data Sparsity, Information Management, Recommender Systems, Social Network