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

Problem definition: 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 and 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. Methodology/results: 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 $1-1/e$ approximation 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 Dating Heuristic (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. Managerial implications: DH-int offers a simple, implementable framework that can substantially improve matching outcomes. Our results provide actionable guidance for curated dating platforms on sequencing, allocation, and leveraging behavioral dynamics. More broadly, the insights extend to other complex, dynamic, two-sided marketplaces—such as freelancing, ride-sharing, and accommodation platforms—where careful sequencing and allocation decisions are critical to optimizing overall outcomes.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1053.