Collaborative topic regression for online recommender systems: an online and Bayesian approach

Abstract

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering the low-dimensional representation of documents and this can result in a sub-optimal representation for prediction. In this paper, we propose a novel inference algorithm, called the Online Bayesian Inference algorithm for CTR model, which is efficient and scalable for learning from data streams. Furthermore, we jointly optimize the combined objective function of both PMF and LDA in an online learning fashion, in which both PMF and LDA tasks can reinforce each other during the online learning process. Our encouraging experimental results on real-world data validate the effectiveness of the proposed method.