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

Asim Ansari (), Yang Li () and Zhaoyong Zhang
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Asim Ansari: Marketing Division, Columbia Business School, Columbia University, New York, New York 10027
Yang Li: Marketing, Cheung Kong Graduate School of Business, Beijing 100738, China

Marketing Science, 2018, vol. 37, issue 6, 987-1008

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, which includes massive textual and numerical data. We therefore develop a novel stochastic variational Bayesian framework to achieve fast, scalable, and accurate estimation in such big-data settings and apply it to a MovieLens data set of movie ratings and semantic tags. We show that our model yields interesting insights about movie preferences and makes much better predictions than a benchmark model that uses only product covariates. We show how our model can be used to target 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 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:37:y:2018:i:6:p:987-1008

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