Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach

50 Pages Posted: 15 Feb 2017 Last revised: 20 Aug 2019

See all articles by Asim Ansari

Asim Ansari

Columbia Business School - Marketing

Yang Li

Cheung Kong Graduate School of Business

Jonathan Z. Zhang

University of Washington - Michael G. Foster School of Business

Abstract

Internet recommender systems are popular in contexts that include heterogeneous consumers and numerous products. In such contexts, product features that adequately describe all the products are often not readily available. Content-based systems therefore rely on user-generated content such as product reviews or textual product tags to make recommendations. In this paper we develop a novel covariate-guided heterogeneous supervised topic model that uses product covariates, user ratings and product tags to succinctly characterize products in terms of latent topics, and specifies consumer preferences via these topics. Recommendation contexts also generate big data problems stemming from data volume, variety and veracity, as in our setting that includes massive textual and numerical data. We therefore develop a novel stochastic variational Bayesian (SVB) framework to achieve fast, scalable and accurate estimation in such big data settings, and apply it on a MovieLens dataset of movie ratings and semantic tags. We show that our model yields interesting insights about movie preferences and predicts much better than a benchmark model that only uses product covariates. We showcase how our model can be used for targeting recommendations to particular users and illustrate its use in generating personalized search rankings of relevant products.

Keywords: Hybrid Recommendation Models, Personalized Search, User-Generated Content, Probabilistic Topic Models, Big Data, Scalable Inference, Stochastic Variational Bayes

JEL Classification: C01, C11, C13, C55, M31

Suggested Citation

Ansari, Asim and Li, Yang and Zhang, Jonathan Z., Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach. Marketing Science, 37(6), 987-1008​, 2018, Available at SSRN: https://ssrn.com/abstract=2916514 or http://dx.doi.org/10.2139/ssrn.2916514

Asim Ansari (Contact Author)

Columbia Business School - Marketing ( email )

New York, NY 10027
United States

Yang Li

Cheung Kong Graduate School of Business ( email )

Oriental Plaza, Tower E2
One East Chang An Avenue
Beijing, Beijing 100738
China
+861085188858 (Phone)

HOME PAGE: http://english.ckgsb.edu.cn/?q=node/381

Jonathan Z. Zhang

University of Washington - Michael G. Foster School of Business ( email )

Box 353200
Seattle, WA 98195-3200
United States

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