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

ACL ID C14-1024
Title Collaborative Topic Regression with Multiple Graphs Factorization for Recommendation in Social Media
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

With a large amount of complex network data available from multiple data sources, how to effec- tively 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 inter- esting 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 frame- work 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 probabilis...