User Interest and Topic Detection for Personalized Recommendation

Recommender system provides users with personalized suggestions of product or information. Typically, recommender systems rely on a bipartite graph model to capture user interest. As an extension, some boosted methods analyze content information to further improve the quality of personalized recommendation. However, due to the prevalence of short and sparse messages in online social media, traditional content-boosted methods do not guarantee to capture user preference accurately especially for web contents. In this paper, we propose a novel graphical model to extract hidden topics from web contents, cluster web contents, and detect users' interests on each cluster. In addition, we introduce two reranking models which utilize the detected user interest to further boost the quality of personalized recommendation. Experiment results on a public dataset demonstrated the limitation of a traditional content-boosted approach, and also showed the validity of our proposed techniques.