Reducing Recommendation Inequality via Two-Sided Matching: A Field Experiment of Online Dating

Abstract

Leading recommender systems may recommend only a small fraction of users on the dating platform since the algorithms often exploit popularity and similarity that reinforce preference homogeneity and assortative mating in the marriage market. We apply a stylized matching model in economics to the existing algorithms to reduce inequality, and we evaluate the proposed method by a large-scale field experiment through a dating app. Experiment results suggest that our recommender reduces inequality, improves predictive accuracy, and leads to substantially more matched couples than other competing algorithms.

Keywords: two-sided matching, recommender systems, reciprocal recommender, inequality, coverage rate, feedback rate