Social network websites, such as Facebook, YouTube, Lastfm etc, have become a
popular platform for users to connect with each other and share content or
opinions. They provide rich information for us to study the influence of user's
social circle in their decision process. In this paper, we are interested in
examining the effectiveness of social network information to predict the user's
ratings of items. We propose a novel hierarchical Bayesian model which jointly
incorporates topic modeling and probabilistic matrix factorization of social
networks. A major advantage of our model is to automatically infer useful
latent topics and social information as well as their importance to
collaborative filtering from the training data. Empirical experiments on two
large-scale datasets show that our algorithm provides a more effective
recommendation system than the state-of-the art approaches. Our results reveal
interesting insight that the social circles have more influence on people's
decisions about the usefulness of information (e.g., bookmarking preference on
Delicious) than personal taste (e.g., music preference on Lastfm). We also
examine and discuss solutions on potential information leak in many
recommendation systems that utilize social information.

本文提出了一种新颖的基于层次贝叶斯模型的社交网络信息效果预测方法，并结合主题建模和概率矩阵分解，从训练数据中自动推断有用的潜在主题和社交信息以及其对协作过滤的重要性，并展现了该算法在推荐系统方面优于现有方法的实验结果，揭示了社交圈在信息的决策过程中比个人品味更具影响力，以及可能在许多利用社交信息的推荐系统中存在的信息泄露问题的解决方案。