Two-sided Assortment Optimization: Adaptivity Gaps and Approximation Algorithms

TL;DR

This paper introduces a two-sided assortment optimization framework, quantifies the adaptivity gaps between policy classes, and develops approximation algorithms with proven performance guarantees.

Contribution

It characterizes the adaptivity gaps between different policy classes and provides polynomial-time algorithms with approximation guarantees for the optimization problem.

Findings

The adaptivity gap between static and adaptive policies is exactly e/(e-1).

The adaptivity gap between adaptive policies and fully adaptive policies is exactly 2.

A polynomial-time algorithm achieves a 1/4 approximation factor for the general problem.

Abstract

To address efficiency and design challenges in choice-based matching platforms, we introduce a two-sided assortment optimization framework under general choice preferences. The goal in this problem is to maximize the expected number of matches by deciding which assortments are displayed to the agents and the order in which they are shown. In this context, we identify several classes of policies that platforms can use in their design. Our goals are: (1) to measure the value that one class of policies has over another one, and (2) to approximately solve the optimization problem itself for a given class. For (1), we define the adaptivity gap as the worst-case ratio between the optimal values of two different policy classes. First, we show that the gap between the class of policies that statically show assortments to one-side first and the class of policies that adaptively show assortments to one-side first is exactly $e/(e-1)$. Second, we show that the gap between the latter class of policies and the fully adaptive class of policies that show assortments to agents one by one is exactly $2$. We also note that the worst policies are those who simultaneously show assortments to all the agents. For (2), we first design a polynomial time algorithm that achieves a $1/4$ approximation factor within the class of policies that adaptively show assortments to agents one by one. Furthermore, when agents' preferences are governed by multinomial-logit models, we show that a 0.067 approximation factor can be obtained within the class of policies that show assortments to all agents at once. We further generalize our results to constrained assortment settings, where we impose an upper bound on the size of the displayed assortments. Finally, we present a computational study to evaluate the empirical performance of our theoretical guarantees.