Improving Match Rates in Dating Markets Through Assortment Optimization

Abstract

Problem definition: We study how online platforms can leverage the behavioral considerations of their users to improve their assortment decisions. Motivated by our collaboration with a dating company, we study how a platform should select the assortments to show to each user in each period to maximize the expected number of matches in a time horizon, considering that a match is formed if two users like each other, possibly on different periods. Academic/Practical Relevance: Increasing match rates is one of the most common objectives among many online platforms. We provide insights on how to leverage users’ behavior towards this end. Methodology: We model the platform’s problem and we use econometric tools to estimate the main inputs of our model, namely, the like and log in probabilities, using our partner’s data. We exploit a change in our partner’s algorithm to estimate the causal effect of previous matches on the like behavior of users. Based on this finding, we propose a family of heuristics to solve for the platform’s problem, and we use simulations and a field experiment to assess the benefits of our algorithm. Results: First, we find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. Leveraging this finding, we propose a family of heuristics that decide the assortment to show to each user on each day. Finally, using simulations and a field experiment we show that our algorithm can yield 40% more matches relative to our partner’s algorithm. Managerial Implications: Our results highlight the importance of correctly accounting for the behavior of users on both ends of a transaction to improve the operational efficiency of matching platforms. In addition, we identify and measure the effect of previous matches in the users’ preferences, which is also leveraged by our algorithm. Our methodology can also be applied to online matching platforms in other settings.