# User-Creator Feature Polarization in Recommender Systems with Dual Influence

arXiv: https://arxiv.org/abs/2407.14094 · HTML: https://mlsanity.com/paper/2407.14094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [Causal Embeddings for Recommendation](https://mlsanity.com/paper/1706.07639/) (2017)
- [KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos](https://mlsanity.com/paper/2208.08696/) (2022)
- [Degenerate Feedback Loops in Recommender Systems](https://mlsanity.com/paper/1902.10730/) (2019)
- [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [Generalized Strategic Classification and the Case of Aligned Incentives](https://mlsanity.com/paper/2202.04357/) (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)
- [How Bad is Top-K Recommendation under Competing Content Creators?](https://mlsanity.com/paper/2302.01971/) (2023)
- [Preference Dynamics Under Personalized Recommendations](https://mlsanity.com/paper/2205.13026/) (2022)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [User Welfare Optimization in Recommender Systems with Competing Content Creators](https://mlsanity.com/paper/2404.18319/) (2024)
- [Online Recommendations for Agents with Discounted Adaptive Preferences](https://mlsanity.com/paper/2302.06014/) (2023)
- [Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset](https://mlsanity.com/paper/2303.11574/) (2023)
- [Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems](https://mlsanity.com/paper/2309.00940/) (2023)
- [Disentangled Representation for Diversified Recommendations](https://mlsanity.com/paper/2301.05492/) (2023)
- [Learning from a Learning User for Optimal Recommendations](https://mlsanity.com/paper/2202.01879/) (2022)
- [Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems](https://mlsanity.com/paper/2401.16641/) (2024)

## Built on by (most cited first, 1 of 1)

- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)

## Abstract (arXiv)

Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: users' preferences are affected by the items they are recommended, while creators may be incentivized to alter their content to attract more users. We define a model, called user-creator feature dynamics, to capture the dual influence of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-k truncation can prevent polarization and improve diversity of the system.