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Operations Research· 2026Q1

Two-Sided Assortment Optimization: Adaptivity Gaps and Approximation Algorithms

Omar El Housni, Ulysse Hennebelle, Alfredo Torrico

Short summary

Platforms gain at most a factor of 2 by updating user options one-by-one (fully adaptive) compared to updating options for only one side of the market (one-sided adaptive).

AI-generated from the title and abstract; the full text is not read.

Key points

  • Adaptivity in updating user options significantly boosts platform performance.
  • Fully adaptive policies (updating for both sides, one by one) yield at most a 2x improvement over one-sided adaptive policies.
  • The theoretical maximum gain from adaptivity within one side of the market is a factor of [Formula: see text].
  • Efficient approximation algorithms were developed and empirically validated.

AI-generated from the title and abstract; the full text is not read.

Abstract

On the Power of Adaptivity in Choice-Based Matching Platforms Online labor, lodging, and carpooling platforms decide not only whom users can choose but also when those options are shown. Simultaneity can crowd attention around a few popular users and leave many good matches unrealized. In the forthcoming Operations Research paper, “Two-Sided Assortment Optimization: Adaptivity Gaps and Approximation Algorithms,” the authors study how much platforms gain by updating assortments after observing earlier choices. They compare four policy classes, ranging from showing all assortments at once to fully adaptive policies that process users one by one. They prove that adaptivity within one side improves performance by at most a factor of [Formula: see text], whereas fully adaptive policies improve on one-sided adaptive policies by at most a factor of two; both bounds are tight. They also develop efficient approximation algorithms, extend their results to constrained settings, and empirically show that their methods perform far better than their worst-case guarantees.

The authors' abstract, as published at the source. Operations Research, 2026 · DOI ↗

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Field: Economics and Econometrics

Economics and EconometricsEconomics, Econometrics and Finance