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Social network websites, such as Facebook, YouTube, Lastfm etc, have become apopular platform for users to connect with each other and share content oropinions. They provide rich information for us to study the influence of user'ssocial circle in their decision process. In this paper, we are interested inexamining the effectiveness of social network information to predict the user'sratings of items. We propose a novel hierarchical Bayesian model which jointlyincorporates topic modeling and probabilistic matrix factorization of socialnetworks. A major advantage of our model is to automatically infer usefullatent topics and social information as well as their importance tocollaborative filtering from the training data. Empirical experiments on twolarge-scale datasets show that our algorithm provides a more effectiverecommendation system than the state-of-the art approaches. Our results revealinteresting insight that the social circles have more influence on people'sdecisions about the usefulness of information (e.g., bookmarking preference onDelicious) than personal taste (e.g., music preference on Lastfm). We alsoexamine and discuss solutions on potential information leak in manyrecommendation systems that utilize social information.

Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems