Optimal Match Recommendations in Two-sided Marketplaces with Endogenous Prices

Abstract

Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are set by the providers based on supply and demand and can depend on the platform’s match recommendation policy. Despite the complications of price endogeneity, an optimal match recommendation policy has a simple structure and can be computed efficiently. In particular, an optimal policy always recommends the providers who deliver the highest conversion rates. Moreover, an optimal policy can be encoded simply in terms of the frequency of recommending each provider to each customer segment, without the need to encode which subsets of providers are to be recommended together. On the other hand, if the platform were to optimize its match recommendations without accounting for price endogeneity, then the resultant policy would be more complex, and the market is likely to get stuck at a strictly suboptimal outcome, even if the platform were to continually reoptimize its match recommendations after prices re-equilibrate. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.02691 .