Balancing Information Perception With Yin-Yang: Agent-based Adaptive Information Neutrality Model for Recommendation Systems
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Journal Article
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Institute of Electrical and Electronics Engineers (IEEE)
Abstract
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of “filter bubbles.” These bubbles restrict the range of information users interact with, inadvertently reinforcing their existing viewpoints. Many studies have been dedicated to improving the recommendation algorithms to tackle this issue. Yet, approaches that maintain the integrity of the original algorithms remain largely unexplored. This article introduces the agent-based adaptive information neutrality (AAIN) model, grounded in Yin-Yang theory. The proposed novel approach targets the imbalance in information perception within existing recommendation systems. It is designed to integrate with these preference-based systems, ensuring the delivery of recommendations with neutral information. Our empirical evaluation of this model proved its effectiveness, showcasing its capacity to expand information diversity while respecting user preferences. Therefore, AAIN proves to be an effective model in reducing the adverse impact of filter bubbles on how information is consumed.Description
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IEEE Transactions on Computational Social Systems, ISSN: 2329-924X (Print); 2329-924X (Online), Institute of Electrical and Electronics Engineers (IEEE), 1-12. doi: 10.1109/tcss.2025.3573074
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the Author's Accepted Manuscript of an article published by IEEE, the version of record available at
https://doi.org/10.1109/tcss.2025.3573074