We study the efficacy of match recommendation in reducing congestion in two-sided matching markets with private preferences. We measure congestion by the number of bits of information about their preferences agents  need to learn and communicate with others to form the final matching. Previous results suggest that a high level of congestion is inevitable under arbitrary preferences.  We show that when the unobservable component of agent preferences satisfy certain natural assumptions, it is possible to recommend potential matches such that 1) agents have negligible incentives to look beyond their set of recommended partners; 2) the market reaches an equilibrium outcome and 3) the overall communication overhead is small. The main idea is to only recommend partners whom the agent has a non-negligible chance of both liking and being liked by, as estimated by the observable component of preferences and prior expression of interest by agents on the other side based on the unobservable component of their preferences.

Joint work with Yash Kanoria, Mark Braverman, and Peng Shi.