A probabilistic framework of preference discovery from folksonomy corpus

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or retrieval results. This paper presents a rigorous probabilistic framework to discover user preference from folksonomy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applicability of the engaged models. The experimental results show that, with the help of the proposed framework, the user preference can be effectively discovered.