Learning Personalized Product Recommendations with Customer Disengagement

50 Pages Posted: 13 Sep 2018 Last revised: 29 Dec 2019

See all articles by Hamsa Bastani

Hamsa Bastani

University of Pennsylvania - The Wharton School

Pavithra Harsha

IBM Research

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Divya Singhvi

Massachusetts Institute of Technology (MIT) - Operations Research Center

Date Written: August 29, 2018

Abstract

We consider the problem of sequential product recommendation when customer preferences are unknown. First, we present empirical evidence of customer disengagement using a sequence of ad campaigns from a major airline carrier. In particular, customers decide to stay on the platform based on the relevance of recommendations. We then formulate this problem as a linear bandit, with the notable difference that the customer's horizon length is a function of past recommendations. We prove that any algorithm in this setting achieves linear regret. Thus, no algorithm can keep all customers engaged; however, we can hope to keep a subset of customers engaged. Unfortunately, we find that classical bandit learning as well as greedy algorithms provably over-explore, thereby incurring linear regret for every customer. We propose modifying bandit learning strategies by constraining the action space upfront using an integer program. We prove that this simple modification allows our algorithm to achieve sublinear regret for a significant fraction of customers. Furthermore, numerical experiments on real movie recommendations data demonstrate that our algorithm can improve customer engagement with the platform by up to 80%.

Keywords: Bandits, Online Learning, Recommendation Systems, Disengagement

Suggested Citation

Bastani, Hamsa and Harsha, Pavithra and Perakis, Georgia and Singhvi, Divya, Learning Personalized Product Recommendations with Customer Disengagement (August 29, 2018). Available at SSRN: https://ssrn.com/abstract=3240970 or http://dx.doi.org/10.2139/ssrn.3240970

Hamsa Bastani

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

Pavithra Harsha

IBM Research ( email )

T. J. Watson Research Center
Yorktown Heights, NY 10598
United States

Georgia Perakis

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-565
Cambridge, MA 02142
United States

Divya Singhvi (Contact Author)

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
194
Abstract Views
1,349
rank
172,510
PlumX Metrics