Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2017

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.

Keywords

Topic modeling, Online learning, Recommender systems, Collaborative filtering

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Machine Learning

Volume

106

Issue

5

First Page

651

Last Page

670

ISSN

0885-6125

Identifier

10.1007/s10994-016-5599-z

Publisher

Springer Verlag (Germany)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s10994-016-5599-z

Download

Share

COinS