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Reducing Recommendation Inequality Via Two‐Sided Matching: A Field Experiment Of Online Dating

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  • Kuan‐Ming Chen
  • Yu‐Wei Hsieh
  • Ming‐Jen Lin

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.

Suggested Citation

  • Kuan‐Ming Chen & Yu‐Wei Hsieh & Ming‐Jen Lin, 2023. "Reducing Recommendation Inequality Via Two‐Sided Matching: A Field Experiment Of Online Dating," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1201-1221, August.
    • Handle: RePEc:wly:iecrev:v:64:y:2023:i:3:p:1201-1221
    DOI: 10.1111/iere.12631
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    References listed on IDEAS

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