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DOI: 10.4230/LIPIcs.FORC.2026.15

Incentivizing High-Quality Content in Online Recommender Systems

Authors: Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract

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.

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Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt. Incentivizing High-Quality Content in Online Recommender Systems. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{hu_et_al:LIPIcs.FORC.2026.15,
  author =	{Hu, Xinyan and Jagadeesan, Meena and Jordan, Michael I. and Steinhardt, Jacob},
  title =	{{Incentivizing High-Quality Content in Online Recommender Systems}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{15:1--15:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.15},
  URN =		{urn:nbn:de:0030-drops-259887},
  doi =		{10.4230/LIPIcs.FORC.2026.15},
  annote =	{Keywords: recommender systems, content quality, producer incentives, online learning, algorithmic game theory, Stackelberg games}
}

Document

DOI: 10.4230/LIPIcs.FORC.2024.9

Privacy Can Arise Endogenously in an Economic System with Learning Agents

Authors: Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, and Michael I. Jordan

Published in: LIPIcs, Volume 295, 5th Symposium on Foundations of Responsible Computing (FORC 2024)

Abstract

We study price-discrimination games between buyers and a seller where privacy arises endogenously - that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equilibrium where some buyers will send a signal that misrepresents their type with some probability; we refer to this as buyer-induced privacy. When the seller is able to publicly commit to providing a certain privacy level, we find that their equilibrium response is to commit to ignore buyers' signals with some positive probability; we refer to this as seller-induced privacy. We then turn our attention to a repeated interaction setting where the game parameters are unknown and the seller cannot credibly commit to a level of seller-induced privacy. In this setting, players must learn strategies based on information revealed in past rounds. We find that, even without commitment ability, seller-induced privacy arises as a result of reputation building. We characterize the resulting seller-induced privacy and seller’s utility under no-regret and no-policy-regret learning algorithms and verify these results through simulations.

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Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, and Michael I. Jordan. Privacy Can Arise Endogenously in an Economic System with Learning Agents. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 9:1-9:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{ananthakrishnan_et_al:LIPIcs.FORC.2024.9,
  author =	{Ananthakrishnan, Nivasini and Ding, Tiffany and Werner, Mariel and Karimireddy, Sai Praneeth and Jordan, Michael I.},
  title =	{{Privacy Can Arise Endogenously in an Economic System with Learning Agents}},
  booktitle =	{5th Symposium on Foundations of Responsible Computing (FORC 2024)},
  pages =	{9:1--9:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-319-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{295},
  editor =	{Rothblum, Guy N.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2024.9},
  URN =		{urn:nbn:de:0030-drops-200921},
  doi =		{10.4230/LIPIcs.FORC.2024.9},
  annote =	{Keywords: Privacy, Game Theory, Online Learning, Price Discrimination}
}

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Documents authored by Jordan, Michael I.

Incentivizing High-Quality Content in Online Recommender Systems

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Privacy Can Arise Endogenously in an Economic System with Learning Agents

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