Learning to Rank an Assortment of Products

87 Pages Posted: 15 Jun 2019 Last revised: 30 Jul 2020

See all articles by Kris Ferreira

Kris Ferreira

Harvard Business School

Sunanda Parthasarathy

Wayfair, Inc.

Shreyas Sekar

University of Washington - Department of Electrical Engineering

Date Written: July 29, 2020

Abstract

We consider the product ranking challenge that online retailers face when their customers typically behave as "window shoppers": they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who engage with the site. Customers' product preferences and attention spans are correlated and unknown to the retailer; furthermore, the retailer cannot exploit similarities across products owing to the presence of subjective, stylistic elements and the fact that products may not be substitutes. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers' clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity: the notion of appealing to a large variety of heterogeneous customers. We prove that our learning algorithms converge to a ranking that matches the best-known approximation factors for the offline, complete information setting. Finally, we partner with Wayfair - a multi-billion dollar home goods online retailer - to estimate the impact of our algorithms in practice via simulations using actual clickstream data, and we find that our algorithms yield a significant increase (5-30%) in the number of customers that engage with the site.

Keywords: e-commerce, product ranking, online learning

Suggested Citation

Ferreira, Kris and Parthasarathy, Sunanda and Sekar, Shreyas, Learning to Rank an Assortment of Products (July 29, 2020). Available at SSRN: https://ssrn.com/abstract=3395992 or http://dx.doi.org/10.2139/ssrn.3395992

Kris Ferreira (Contact Author)

Harvard Business School ( email )

Boston, MA 02163
United States
617-495-3316 (Phone)

HOME PAGE: http://www.hbs.edu/faculty/Pages/profile.aspx?facId=773347

Sunanda Parthasarathy

Wayfair, Inc. ( email )

Shreyas Sekar

University of Washington - Department of Electrical Engineering

United States

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

Paper statistics

Downloads
511
Abstract Views
2,193
Rank
102,167
PlumX Metrics