Socially-Aware Recommender Systems Mitigate Opinion Clusterization

2026-01-02

arXiv.org

AI Summary

This work addresses the tendency of recommender systems to reinforce feedback loops among users and content creators, which can exacerbate filter bubbles and opinion polarization, leading to opinion clustering. To counter this, the authors propose a novel recommendation mechanism that explicitly incorporates the topological structure of users’ social networks into the algorithm design. By modeling dynamic user–creator feedback and strategically injecting content diversity, the approach effectively balances personalization with viewpoint heterogeneity. Experimental results demonstrate that the proposed method significantly mitigates the homogenizing effect of recommendations on users’ opinions and effectively suppresses opinion clustering, thereby validating the potential of social-aware recommendation strategies in fostering viewpoint diversity.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

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.

Problem

• recommender systems
• opinion polarization
• filter bubbles
• opinion clusterization
• social networks

Innovation

• socially-aware recommender system
• opinion polarization
• filter bubble
• social network topology
• content diversification