Discriminative factored prior models for personalized content-based recommendation

Most existing content-based filtering approaches including Rocchio, Language Models, SVM, Logistic Regression, Neural Networks, etc. learn user profiles independently without capturing the similarity among users. The Bayesian hierarchical models learn user profiles jointly and have the advantage of being able to borrow information from other users through a Bayesian prior. The standard Bayesian hierarchical model used in filtering assumes all user profiles are generated from the same Gaussian prior. However, considering the diversity of user interests, this assumption might not be optimal. Besides, most existing content-based filtering approaches implicitly assume that each user profile corresponds to exactly one user interest and fail to capture a user’s multiple interests (information needs). In this paper, we present a flexible Bayesian hierarchical modeling approach, which we call Discriminative Factored Prior Models (DFPM), to model both commonality and diversity among ...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Lanbo Zhang, Yi Zhang 0001
• Keywords: Bayesian Hierarchical Model, CIKM 2010, Content-based filtering Approaches, Information Technology, User Profile