Jeon, Doh-Shin and Drugov, Mikhail (2026) Dynamic Recommendation Bias. TSE Working Paper, n. 26-1742, Toulouse

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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.