The Dating Heuristic: A Provably Strong Matching Algorithm for Dating Platforms

TL;DR

This paper introduces a new matching algorithm for online dating platforms that guarantees near-optimal performance by balancing initial and follow-up interactions, validated through theoretical analysis and empirical data.

Contribution

The paper develops a provably strong, theoretically grounded heuristic for dynamic matching in dating platforms, addressing the two-sided market and user history effects.

Findings

DH-int achieves a 1-1/e performance guarantee.

Empirical results show DH-int outperforms benchmarks.

DH-int approaches the theoretical upper bound in real data.

Abstract

Motivated by online dating platforms, we study the problem of selecting which subset of profiles to display to each user in each period. Users observe the profiles set by the platform, decide which of them to like, and a match occurs if and only if two users mutually like each other, potentially across different periods. The platform aims to maximize the expected number of matches produced over the entire time horizon, and users' behavior -- captured by their like probabilities -- may depend on their history. We develop a general theoretical model that captures the dynamic, two-sided nature of the problem and the influence of users' past experiences on their future behavior. We focus on one-lookahead policies and propose the Integral Dating Heuristic (DH-int), providing formal performance guarantees: DH-int achieves a uniform $1-1/e$ guarantee across all platform designs under reasonable assumptions. Our empirical analysis, using proprietary data from a major U.S.-based dating app, confirms that DH-int consistently outperforms other benchmarks such as Greedy, Perfect Matching and DH, and approaches the theoretical upper bound across multiple platform designs and variants of the history effect. The superior performance of DH-int is driven primarily by its careful balancing of initial and follow-up interactions, which accounts for the two-sided nature of the market.