Improving Match Rates in Dating Markets Through Assortment Optimization
Information Systems and Operations management (ISOM)
Speaker : Daniela SABAN
from Stanford GSB/USC Marshall
HEC Campus - Room Bernard Ramanantsoa
Motivated by our collaboration with an online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period in order to maximize the expected number of matches in a time horizon, where a match is formed only after two users like each other, possibly in different periods. Our work combines several methodologies. We model the platform's problem as a dynamic optimization problem. We use econometric tools and exploit a change in the company's algorithm in order to estimate the users' preferences as well as other parameters of interest. We find that the number of matches obtained in the recent past has a negative effect on the like behavior of users. We propose a family of heuristics to solve the platform's problem that leverage these data findings, and use simulations to assess their benefits. Two field experiments show that our algorithm yields at least 27% more matches relative to our industry partner's algorithm. Overall, our results highlight the importance of correctly accounting for the preferences, behavior, and activity metrics of users on both ends of a transaction to improve the operational efficiency of matching platforms.