Collaborative Topic Regression with Multiple Graphs Factorization for Recommendation in Social Media

With a large amount of complex network data available from multiple data sources, how to effectively combine these available data with existing auxiliary information such as item content into the same recommendation framework for more accurately modeling user preference is an interesting and significant research topic for various recommender systems. In this paper, we propose a novel hierarchical Bayesian model to integrate multiple social network structures and content information for item recommendation. The key idea is to formulate a joint optimization framework to learn latent user and item representations, with simultaneously learned social factors and latent topic variables. The main challenge is how to exploit the shared information among multiple social graphs in a probabilistic framework. To tackle this challenge, we incorporate multiple graphs probabilistic factorization with two alternatively designed combination strategies into collaborative topic regression (CTR). Experimental results on real dataset demonstrate the effectiveness of our approach.