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Dynamic Recommendation Bias

Author

Listed:
  • Jeon, Doh-Shin
  • Drugov, Mikhail

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 dis tinct incentives to bias recommendations: improving the platform’s future bargain ing 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

  • Jeon, Doh-Shin & Drugov, Mikhail, 2026. "Dynamic Recommendation Bias," TSE Working Papers 26-1742, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:131694
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    References listed on IDEAS

    as
    1. Alexandre de Cornière & Greg Taylor, 2019. "A model of biased intermediation," RAND Journal of Economics, RAND Corporation, vol. 50(4), pages 854-882, December.
    2. Adam Brandenburger & Ben Polak, 1996. "When Managers Cover Their Posteriors: Making the Decisions the Market Wants to See," RAND Journal of Economics, The RAND Corporation, vol. 27(3), pages 523-541, Autumn.
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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L42 - Industrial Organization - - Antitrust Issues and Policies - - - Vertical Restraints; Resale Price Maintenance; Quantity Discounts

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