Balancing Information Perception With Yin-Yang: Agent-based Adaptive Information Neutrality Model for Recommendation Systems

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.