Clearing matching markets efficiently: Informative signals and match recommendations

Itai Ashlagi, Mark Braverman, Yash Kanoria, Peng Shi

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


We study how to reduce congestion in two-sided matching markets with private preferences. We measure congestion by the number of bits of information that agents must (i) learn about their own preferences, and (ii) communicate with others before obtaining their final match. Previous results suggest that a high level of congestion is inevitable under arbitrary preferences before the market can clear with a stable matching. We show that when the unobservable component of agent preferences satisfies certain natural assumptions, it is possible to recommend potential matches and encourage informative signals such that the market reaches a stable matching with a low level of congestion. Moreover, under our proposed approach, agents have negligible incentive to leave the marketplace or to look beyond the set of recommended partners. The intuitive idea is to only recommend partners with whom there is a nonnegligible chance that the agent will both like them and be liked by them. The recommendations are based on both the observable component of preferences and signals sent by agents on the other side that indicate interest.

Original languageEnglish (US)
Pages (from-to)2163-2193
Number of pages31
JournalManagement Science
Issue number5
StatePublished - May 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research


  • Communication complexity
  • Informative signaling
  • Marketplace and platform design
  • Match recommendations
  • Stable matching


Dive into the research topics of 'Clearing matching markets efficiently: Informative signals and match recommendations'. Together they form a unique fingerprint.

Cite this