Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911829739962368 |
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| author | Wang, Mengyan Hu, Yuxuan Wu, Shiqing Li, Weihua Bai, Quan Rupar, Verica |
| author_facet | Wang, Mengyan Hu, Yuxuan Wu, Shiqing Li, Weihua Bai, Quan Rupar, Verica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04906 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems Wang, Mengyan Hu, Yuxuan Wu, Shiqing Li, Weihua Bai, Quan Rupar, Verica Human-Computer Interaction Information Retrieval 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. |
| title | Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems |
| topic | Human-Computer Interaction Information Retrieval |
| url | https://arxiv.org/abs/2404.04906 |