Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences

40 Pages Posted: 31 May 2022

See all articles by Fernando Bernstein

Fernando Bernstein

Duke University

Sajad Modaresi

University of North Carolina at Chapel Hill - Kenan-Flagler Business School

Denis Saure

University of Chile - Industrial Engineering

Date Written: April 27, 2022

Abstract

We study efficient real-time data collection for an online retailer that dynamically personalizes assortments based on customers’ attributes to learn their preferences and maximize revenue. Prior work on personalization in the operations management and marketing literature generally assumes a linear relationship between product utilities and customer attributes. An important implication of this assumption, which has not received much attention in the literature, is that one can infer a customer’s preference for a product using transaction data from other customers. In other words, demand learning can be shared across customer profiles. We leverage this insight to study the structure of efficient exploration in an online assortment personalization setting. We prove a lower bound on the asymptotic regret of any admissible policy and show that not all products and customer profiles need to be explored in order to estimate customer demand. We apply this insight to design efficient learning policies. In particular, we propose adaptive learning policies that solve a linear mixed integer program, called the exploration-optimization problem, to identify an efficient exploration set which determines what assortments to display to which customer profiles. To illustrate the practical value of the proposed policies, we consider a setting calibrated using a dataset from a large Chilean retailer. We compare the performance of our policies to that of Thompson sampling and show that there is a significant gain from using our proposed policies as they focus exploration efforts on the “right” subset of products and customer profiles.

Keywords: Dynamic Assortment Planning, Personalization, Online Retailing, Multi-Armed Bandit

Suggested Citation

Bernstein, Fernando and Modaresi, Sajad and Saure, Denis, Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences (April 27, 2022). Available at SSRN: https://ssrn.com/abstract=4115721 or http://dx.doi.org/10.2139/ssrn.4115721

Fernando Bernstein

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Sajad Modaresi (Contact Author)

University of North Carolina at Chapel Hill - Kenan-Flagler Business School ( email )

300 Kenan Drive
Chapel Hill, NC 27599
United States

Denis Saure

University of Chile - Industrial Engineering ( email )

República 701, Santiago
Chile

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
103
Abstract Views
443
Rank
461,752
PlumX Metrics