Dynamic Recommendation Bias

Mikhail Drugov and Doh-Shin Jeon

No 21451, CEPR Discussion Papers from Centre for Economic Policy Research

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 content 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.

Keywords: Platform (search for similar items in EconPapers)
JEL-codes: D83 L42 (search for similar items in EconPapers)
Date: 2026-05
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