Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network

User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers' study in recent years. Most previous studies inferred users' interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user's interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.