2026-09-18T20:59:43Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25988 2026-09-05T19:32:00Z ddc:004 open_access

Incentivizing High-Quality Content in Online Recommender Systems Hu, Xinyan Jagadeesan, Meena Jordan, Michael I. Steinhardt, Jacob recommender systems content quality producer incentives online learning algorithmic game theory Stackelberg games In content recommender systems such as TikTok and YouTube, the platform’s recommendation algorithm shapes content producer incentives. Many platforms employ online learning, which generates intertemporal incentives, since content produced today affects recommendations of future content. We study the game between producers and analyze the content created at equilibrium. We prove that standard online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content, where producers' effort approaches zero in the long run for typical learning rate schedules. Motivated by this negative result, we design learning algorithms that incentivize producers to invest high effort and achieve high user welfare. At a conceptual level, our work illustrates the unintended impact that a platform’s learning algorithm can have on content quality and introduces algorithmic approaches to mitigating these effects. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Xinyan Hu and Meena Jagadeesan and Michael I. Jordan and Jacob Steinhardt 2026 Is Part Of LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2026.15 urn:nbn:de:0030-drops-259887 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.15 eng https://creativecommons.org/licenses/by/4.0/legalcode