Dynamic Recommendation Bias

Doh-Shin Jeon and Mikhail Drugov

No 26-1742, TSE Working Papers from Toulouse School of Economics (TSE)

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.

JEL-codes: D83 L42 (search for similar items in EconPapers)
Date: 2026-04-29
New Economics Papers: this item is included in nep-com, nep-des and nep-mic
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:131694

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