Online discrete choice models: Applications in personalized recommendations

• Published in: Decision Support Systems 2019-04, Vol.119, p.35-45
• Main Authors: Danaf, Mazen, Becker, Felix, Song, Xiang, Atasoy, Bilge, Ben-Akiva, Moshe
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Computer simulation; Economic conditions; Embedded systems; Hierarchical Bayes; Intra-consumer heterogeneity; Levels; Mathematical models; Monte Carlo simulation; Optimization; Parameter estimation; Personalization; Preference updates, recommender systems; Real time; Recommender systems
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2019.02.003

This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing random taste variations among individuals and among choice situations (menus) for a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual-level and menu-specific. In the context of a recommender system, the estimation of these parameters is repeated periodically in an offline process in order to account for trends, such as changing market conditions. Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate the latest information from the users. This online update is computationally efficient which makes it feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various recommender systems that generate personalized recommendations ranging from transportation to e-commerce and online marketing, but is particularly useful when the attributes of the alternatives vary over time. •We present a framework for estimating and updating user preferences in an app-based recommendation system.•A Hierarchical Bayes estimator is proposed in order to account for inter- and intra-personal heterogeneity.•An online procedure is used to update user preferences in real-time upon making a choice.•Monte-Carlo data and SP data on transportation mode choice are used to demonstrate the online preference estimation.