# How Bad is Top-K Recommendation under Competing Content Creators?

arXiv: https://arxiv.org/abs/2302.01971 · HTML: https://mlsanity.com/paper/2302.01971/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [What game are we playing? End-to-end learning in normal and extensive form games](https://mlsanity.com/paper/1805.02777/) (2018)
- [The Price of Anarchy in Auctions](https://mlsanity.com/paper/1607.07684/) (2016)
- [From Recommendation Systems to Facility Location Games](https://mlsanity.com/paper/1809.02931/) (2018)
- [Lost Relatives of the Gumbel Trick](https://mlsanity.com/paper/1706.04161/) (2017)
- [Hotelling-Downs Model with Limited Attraction](https://mlsanity.com/paper/1611.05959/) (2016)
- [Shapley Facility Location Games](https://mlsanity.com/paper/1709.10278/) (2017)

## Built on by (most cited first, 27 of 27)

- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [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)
- [A Market for Accuracy: Classification under Competition](https://mlsanity.com/paper/2502.18052/) (2025)
- [Strategic Linear Contextual Bandits](https://mlsanity.com/paper/2406.00551/) (2024)
- [Contractual Reinforcement Learning: Pulling Arms with Invisible Hands](https://mlsanity.com/paper/2407.01458/) (2024)
- [When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making](https://mlsanity.com/paper/2308.11721/) (2023)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Multi-Round Human-AI Collaboration with User-Specified Requirements](https://mlsanity.com/paper/2602.17646/) (2026)
- [Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts](https://mlsanity.com/paper/2309.13896/) (2023)
- [Policy Design for Two-sided Platforms with Participation Dynamics](https://mlsanity.com/paper/2502.01792/) (2025)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?](https://mlsanity.com/paper/2502.20783/) (2025)
- [Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits](https://mlsanity.com/paper/2604.08643/) (2026)
- [User-Creator Feature Polarization in Recommender Systems with Dual Influence](https://mlsanity.com/paper/2407.14094/) (2024)
- [Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs](https://mlsanity.com/paper/2503.03606/) (2025)
- [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)
- [Fraud-Proof Revenue Division on Subscription Platforms](https://mlsanity.com/paper/2511.04465/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)
- [Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry](https://mlsanity.com/paper/2602.17787/) (2026)
- [Market Design for AI: Beyond the Copyright Binary](https://mlsanity.com/paper/2606.12260/) (2026)
- [The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape](https://mlsanity.com/paper/2607.25253/) (2026)
- [CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments](https://mlsanity.com/paper/2609.27273/) (2026)

## Abstract (arXiv)

Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. This work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-K recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on K and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.