# Socially-Aware Recommender Systems Mitigate Opinion Clusterization

arXiv: https://arxiv.org/abs/2601.02412 · HTML: https://mlsanity.com/paper/2601.02412/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020)
- [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)
- [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)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I](https://mlsanity.com/paper/1701.06307/) (2017)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice](https://mlsanity.com/paper/2407.13699/) (2024)
- [The closed loop between opinion formation and personalised recommendations](https://mlsanity.com/paper/1809.04644/) (2018)
- [Performative Recommendation: Diversifying Content via Strategic Incentives](https://mlsanity.com/paper/2302.04336/) (2023)
- [Modeling confirmation bias and polarization](https://mlsanity.com/paper/1607.00022/) (2016)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Novel Multidimensional Models of Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1505.04920/) (2015)
- [User Welfare Optimization in Recommender Systems with Competing Content Creators](https://mlsanity.com/paper/2404.18319/) (2024)
- [On the Relationship Between Relevance and Conflict in Online Social Link Recommendations](https://mlsanity.com/paper/2310.14076/) (2023)
- [Trust and Trustworthiness in Social Recommender Systems](https://mlsanity.com/paper/1903.01780/) (2019)
- [Harm Mitigation in Recommender Systems under User Preference Dynamics](https://mlsanity.com/paper/2406.09882/) (2024)
- [Towards Consensus: Reducing Polarization by Perturbing Social Networks](https://mlsanity.com/paper/2206.08996/) (2022)
- [Disentangled Representation for Diversified Recommendations](https://mlsanity.com/paper/2301.05492/) (2023)
- [The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects](https://mlsanity.com/paper/2309.08967/) (2023)
- [Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design](https://mlsanity.com/paper/2502.12973/) (2025)
- [Rethinking the Micro-Foundation of Opinion Dynamics: Rich Consequences of the Weighted-Median Mechanism](https://mlsanity.com/paper/1909.06474/) (2019)
- [Control Strategies for Recommendation Systems in Social Networks](https://mlsanity.com/paper/2403.06152/) (2024)
- [How to Diversify any Personalized Recommender?](https://mlsanity.com/paper/2405.02156/) (2024)
- [User-Creator Feature Polarization in Recommender Systems with Dual Influence](https://mlsanity.com/paper/2407.14094/) (2024)
- [Modelling the Closed Loop Dynamics Between a Social Media Recommender System and Users' Opinions](https://mlsanity.com/paper/2507.19792/) (2025)

## Abstract (arXiv)

Recommender systems shape online interactions by matching users with creators content to maximize engagement. Creators, in turn, adapt their content to align with users preferences and enhance their popularity. At the same time, users preferences evolve under the influence of both suggested content from the recommender system and content shared within their social circles. This feedback loop generates a complex interplay between users, creators, and recommender algorithms, which is the key cause of filter bubbles and opinion polarization. We develop a social network-aware recommender system that explicitly accounts for this user-creators feedback interaction and strategically exploits the topology of the user's own social network to promote diversification. Our approach highlights how accounting for and exploiting user's social network in the recommender system design is crucial to mediate filter bubble effects while balancing content diversity with personalization. Provably, opinion clusterization is positively correlated with the influence of recommended content on user opinions. Ultimately, the proposed approach shows the power of socially-aware recommender systems in combating opinion polarization and clusterization phenomena.