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.

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.

Fanyin Zheng, Daniela Saban, and Ignacio Rios
Journal Article
Publication Date

Full Citation

Zheng, Fanyin, Daniela Saban, and Ignacio Rios
. “Improving Match Rates in Dating Markets Through Assortment Optimization.” (December 13, 2021).