Probabilistic factor models for web site recommendation

Due to the prevalence of personalization and information filtering applications, modeling users' interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques. We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources. The proposed method is general, and can be applied to not only Web site recommendations, but also a wide range of Web applications, including behavioral targeting, sponsored search, etc. The experimental analysis on Web site recommendation shows that our method outperforms other traditional recommendation approaches. Moreover, the complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations.