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A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers

arXiv 1806.00955 ↗First posted 4 Jun 2018cs.GT cs.IRcited by 33

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Abstract (arXiv)

We introduce a game-theoretic approach to the study of recommendation systems with strategic content providers. Such systems should be fair and stable. Showing that traditional approaches fail to satisfy these requirements, we propose the Shapley mediator. We show that the Shapley mediator fulfills the fairness and stability requirements, runs in linear time, and is the only economically efficient mechanism satisfying these properties.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2018.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 9; 2024: 8; 2025: 3; 2026: 4.

33 papers.

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  1. 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 77
  2. 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
  3. 2022Supply-Side Equilibria in Recommender Systemscited by 33
  4. 2023How Bad is Top-K Recommendation under Competing Content Creators?cited by 27
  5. 2021Auctions Between Regret-Minimizing Agentscited by 22
  6. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  7. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  8. 2021How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agentscited by 17
  9. 2024Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platformscited by 13
  10. 2021T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systemscited by 12
  11. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  12. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
21 more
  1. 2021Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilitiescited by 9
  2. 2023Learning with Exposure Constraints in Recommendation Systemscited by 8
  3. 2023Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systemscited by 7
  4. 2023Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Modelscited by 7
  5. 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
  6. 2023Competing for Shareable Arms in Multi-Player Multi-Armed Banditscited by 6
  7. 2023Incentivizing High-Quality Content in Online Recommender Systemscited by 6
  8. 2023Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystemscited by 5
  9. 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
  10. 2023Decongestion by Representation: Learning to Improve Economic Welfare in Marketplacescited by 3
  11. 2024On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspectivecited by 2
  12. 2025Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?cited by 2
  13. 2024User-Creator Feature Polarization in Recommender Systems with Dual Influencecited by 1
  14. 2022Recommendation with User Active Disclosing Willingnesscited by 0
  15. 2024PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creatorscited by 0
  16. 2025Data Sharing with a Generative AI Competitorcited by 0
  17. 2025Entry Barriers in Content Marketscited by 0
  18. 2026Socially-Aware Recommender Systems Mitigate Opinion Clusterizationcited by 0
  19. 2026Content Creation with Spillovers: An Incentive Design Approachcited by 0
  20. 2026The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shapecited by 0
  21. 2026Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomescited by 0