Online Assortment Optimization for Two-sided Matching Platforms

56 Pages Posted: 7 Jan 2021 Last revised: 10 Mar 2021

See all articles by Ali Aouad

Ali Aouad

London Business School

Daniela Saban

Stanford Graduate School of Business

Date Written: October 15, 2020

Abstract

Motivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers, and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests he has received and, based on his preferences, he chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We show that, when suppliers do not immediately accept/reject match requests, our problem is fundamentally different from the standard (one-sided) assortment problem, where customers choose over a set of products. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers' arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers' preferences are described by the Multinomial Logit and Nested Logit choice models. We develop specialized balancing algorithms, which we call preference-aware, that leverage general information about the suppliers' choice models. In certain settings, the resulting competitive ratios are provably larger than the standard "barrier" of 1-1/e in the adversarial arrival model. Overall, our results suggest that the shape and timing of suppliers' preferences play critical roles in designing online two-sided assortment algorithms.

Keywords: assortment optimization, matching markets, online platforms, online algorithms

Suggested Citation

Aouad, Ali and Saban, Daniela, Online Assortment Optimization for Two-sided Matching Platforms (October 15, 2020). Available at SSRN: https://ssrn.com/abstract=3712553 or http://dx.doi.org/10.2139/ssrn.3712553

Ali Aouad

London Business School ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

Daniela Saban (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

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