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Dynamic Recommendation Bias

Author

Listed:
  • Mikhail Drugov

    (UAB - Universitat Autònoma de Barcelona = Autonomous University of Barcelona = Universidad Autónoma de Barcelona)

  • Doh-Shin Jeon

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

This paper studies the incentives of a subscription-funded platform that offers both proprietary and third-party content to bias its recommendations about which con tent users should consume. Consistent with Netflix's practice, we consider fixed-fee bargaining between the platform and a content provider, which eliminates any static incentive to bias recommendations. However, our dynamic model identifies two distinct incentives to bias recommendations: improving the platform's future bargaining position and increasing users' expected surplus. The former favors first-party content, while the latter favors the ex ante superior content. As a result, biased recommendations may lead to either self-preferencing or third-party preferencing.

Suggested Citation

  • Mikhail Drugov & Doh-Shin Jeon, 2026. "Dynamic Recommendation Bias," Working Papers hal-05610431, HAL.
    • Handle: RePEc:hal:wpaper:hal-05610431
    Note: View the original document on HAL open archive server: https://hal.science/hal-05610431v1
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    File URL: https://hal.science/hal-05610431v1/document
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