Learning Customer Preferences from Personalized Assortments

54 Pages Posted: 7 Aug 2018

See all articles by Yifan Feng

Yifan Feng

University of Chicago - Booth School of Business

Rene Caldentey

University of Chicago - Booth School of Business

Christopher Thomas Ryan

University of Chicago - Booth School of Business

Date Written: July 17, 2018

Abstract

A company wishes to identify the most popular version of a product from a menu of alternative options. Unaware of customers' true preferences, the company relies on a feedback system that allows potential buyers to provide feedback on their preferred versions. Under a general ranking-based choice model framework, we study how to dynamically individualize the set of versions shown to each customer for them to provide feedback on. This allows the company to identify the top-ranked version with a fixed probabilistic confidence level using a minimal amount of feedback. We prove an instance-specific lower bound on the sample complexity and propose a sampling policy (Myopic Tracking Policy), which is both asymptotically optimal and intuitive to implement. Our methodology draws on previous work in the sequential design of experiments and best arm identification. We illustrate our methodology using a special class of choice models based on Luce's (1959) attraction model and provide a simple closed-form solution that reveals a number of key properties of our proposed Myopic Tracking policy.

Keywords: sequential learning, maximum selection, best arm identification, personalized assortment planning

Suggested Citation

Feng, Yifan and Caldentey, Rene and Ryan, Christopher Thomas, Learning Customer Preferences from Personalized Assortments (July 17, 2018). Available at SSRN: https://ssrn.com/abstract=3215614 or http://dx.doi.org/10.2139/ssrn.3215614

Yifan Feng (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

HOME PAGE: http://sites.google.com/view/yifanfeng/

Rene Caldentey

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

HOME PAGE: http://www.chicagobooth.edu/faculty/directory/c/rene-caldentey

Christopher Thomas Ryan

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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