Online discrete choice models: Applications in personalized recommendations

Mazen Danaf, Felix Becker, Xiang Song, Bilge Atasoy, Moshe Ben-Akiva

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)35-45
JournalDecision Support Systems
Volume119
DOIs
Publication statusPublished - 2019

Keywords

  • Hierarchical Bayes
  • Intra-consumer heterogeneity
  • Personalization
  • Preference updates, recommender systems

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