Collaborative Topic Regression for Online Recommender Systems:
An Online and Bayesian Approach

Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

Abstract

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (e.g., LDA) for recommender systems, which has gained increasing successes in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for CTR model (bdi-CTR) has some critical limitations. First of all, it is designed to work in a batch learning manner, making them unsuitable to deal with streaming data or big data in real-world recommender systems. Second, the item-specific topic proportions of LDA are fed to the downstream PMF, but not reverse, which is sub-optimal as the rating information is not exploited in discovering the low-dimensional representation of documents and thus 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 (obi-CTR), which is efficient and scalable for learning from data streams. Particularly, we {\it 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. 

Publication
  • "Collaborative Topic Regression for Online Recommender Systems: An Online and Bayesian Approach" Chenghao Liu, Tao Jin, Steven C.H. Hoi, Peilin Zhao, Jianling Sun, Machine Learning, 2017. [ PDF ] (preprint)

 

Datasets/Code in Our Experiments


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