Discovering user preference from folksonomy

The increasing availability of socially shared media with tags annotated makes it vital for retrieval approaches to precisely detect web content topic semantic and better understand user interest. Most existing methodologies process the queries merely considering user posted keywords and retrieve media labeled with tags that are similar to query words, while ignoring users implicit interests and preferences. This fact stimulates us to develop preference discovering models to reveal the users' latent intents. In this paper, we study the problem of finding user preference and interest from folksonomy corpus and propose a preference-topic model that exploits probabilistic graphical model and Gibbs sampling algorithm to infer the user interested latent semantic topics. The experimental results show that, with the help of the proposed model, preference topics of the web content creators can be effectively discovered. In addition, two exemplified applications are discussed briefly.