Celebrity Recommendation with Collaborative Social Topic Regression

Recently how to recommend celebrities to the public becomes an interesting problem on the social network websites, such as Twitter and Tencent Weibo. In this paper, we proposed a unified hierarchical Bayesian model to recommend celebrities to the general users. Specifically, we proposed to leverage both social network and descriptions of celebrities to improve the prediction ability and recommendation interpretability. In our model, we combine topic model with matrix factorization for both social network of celebrities and user following action matrix. It works by regularizing celebrity factors through celebrity's social network and descriptive words associated with each celebrity. We also proposed to incorporate different confidences for different dyadic contexts to handle the situation that only positive observations exist. We conducted experiments on two real-world datasets from Twitter and Tencent Weibo, which are the largest and second largest microblog websites in USA and China, respectively. The experiment results show that our model achieves a higher performance and provide more effective results than the state-of-art methods especially when recommending new celebrities. We also show that our model captures user intertests more precisely and gives better recommendation interpretability.