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

arXiv: https://arxiv.org/abs/1806.00955 · HTML: https://mlsanity.com/paper/1806.00955/

## Key facts

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

## Built on by (most cited first, 30 of 33)

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- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Auctions Between Regret-Minimizing Agents](https://mlsanity.com/paper/2110.11855/) (2021)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Fair ranking: a critical review, challenges, and future directions](https://mlsanity.com/paper/2201.12662/) (2022)
- [How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents](https://mlsanity.com/paper/2112.07640/) (2021)
- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [T-RECS: A Simulation Tool to Study the Societal Impact of Recommender Systems](https://mlsanity.com/paper/2107.08959/) (2021)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities](https://mlsanity.com/paper/2105.02377/) (2021)
- [Learning with Exposure Constraints in Recommendation Systems](https://mlsanity.com/paper/2302.01377/) (2023)
- [Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems](https://mlsanity.com/paper/2305.17428/) (2023)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists](https://mlsanity.com/paper/2404.04269/) (2024)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Incentivizing High-Quality Content in Online Recommender Systems](https://mlsanity.com/paper/2306.07479/) (2023)
- [Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems](https://mlsanity.com/paper/2309.00940/) (2023)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces](https://mlsanity.com/paper/2306.10606/) (2023)
- [On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective](https://mlsanity.com/paper/2411.13052/) (2024)
- [Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?](https://mlsanity.com/paper/2502.20783/) (2025)
- [User-Creator Feature Polarization in Recommender Systems with Dual Influence](https://mlsanity.com/paper/2407.14094/) (2024)
- [Recommendation with User Active Disclosing Willingness](https://mlsanity.com/paper/2211.01155/) (2022)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)
- [Data Sharing with a Generative AI Competitor](https://mlsanity.com/paper/2505.12386/) (2025)
- [Entry Barriers in Content Markets](https://mlsanity.com/paper/2509.01953/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)

## 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.