# Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems

arXiv: https://arxiv.org/abs/2309.00940 · HTML: https://mlsanity.com/paper/2309.00940/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [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)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Fairness of Exposure in Light of Incomplete Exposure Estimation](https://mlsanity.com/paper/2205.12901/) (2022)
- [From Recommendation Systems to Facility Location Games](https://mlsanity.com/paper/1809.02931/) (2018)
- [Multi-Robot Routing with Time Windows: A Column Generation Approach](https://mlsanity.com/paper/2103.08835/) (2021)

## Built on by (most cited first, 5 of 5)

- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Policy Design for Two-sided Platforms with Participation Dynamics](https://mlsanity.com/paper/2502.01792/) (2025)
- [User-Creator Feature Polarization in Recommender Systems with Dual Influence](https://mlsanity.com/paper/2407.14094/) (2024)
- [PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators](https://mlsanity.com/paper/2403.15524/) (2024)

## Abstract (arXiv)

Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a comprehensive view of user preferences across the entire user base, content providers, by contrast, generally have only a local view of the preferences of users that have interacted with their content. This limits a provider's ability to offer new content to best serve the broader population. In this work, we tackle this information asymmetry with content prompting policies. A content prompt is a hint or suggestion to a provider to make available novel content for which the RS predicts unmet user demand. A prompting policy is a sequence of such prompts that is responsive to the dynamics of a provider's beliefs, skills and incentives. We aim to determine a joint prompting policy that induces a set of providers to make content available that optimizes user social welfare in equilibrium, while respecting the incentives of the providers themselves. Our contributions include: (i) an abstract model of the RS ecosystem, including content provider behaviors, that supports such prompting; (ii) the design and theoretical analysis of sequential prompting policies for individual providers; (iii) a mixed integer programming formulation for optimal joint prompting using path planning in content space; and (iv) simple, proof-of-concept experiments illustrating how such policies improve ecosystem health and user welfare.