  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.
