Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems

TL;DR

This paper proposes the AbIN agent-based model based on Yin-Yang theory to promote information neutrality in recommendation systems, effectively increasing diversity without compromising user preferences.

Contribution

It introduces a novel Yin-Yang inspired agent-based model that maintains recommendation quality while enhancing information diversity.

Findings

Increased information diversity in recommendations.

Maintained user preference satisfaction.

Reduced filter bubble effects.

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. Previous research has focused on modifying these underlying algorithms to tackle this issue. Yet, approaches that maintain the integrity of the original algorithms remain largely unexplored. This paper introduces an Agent-based Information Neutrality model grounded in the Yin-Yang theory, namely, AbIN. This innovative 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 efficacy, showcasing its capacity to expand information diversity while respecting user preferences. Consequently, AbIN emerges as an instrumental tool in mitigating the negative impact of filter bubbles on information consumption.