REDUCING RECOMMENDATION INEQUALITY VIA TWO‐SIDED MATCHING: A FIELD EXPERIMENT OF ONLINE DATING

Kuan‐Ming Chen, Yu‐Wei Hsieh and Ming‐Jen Lin

International Economic Review, 2023, vol. 64, issue 3, 1201-1221

Abstract: Leading dating platforms usually recommend only a small fraction of users based on users' popularity and similarity, leading to recommendation inequality. We use a stylized matching model from economics to modify existing algorithms to reduce inequality. We evaluate the proposed method through a large‐scale field experiment on a dating platform. Experiment results suggest that our recommender reduces inequality, improves predictive accuracy, and leads to substantially more matched couples than other competing algorithms.

Date: 2023
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