Abstract
AI-based recommendation approaches can contribute to the formation of ideological isolation, reinforcing users’ existing beliefs and limiting exposure to diverse perspectives. Additionally, different Human-Computer Interaction (HCI) approaches may have varying impacts on the diversity of users’ information consumption as well. This study focuses on three HCI approaches, namely Search, Click and Scroll, with users exclusively engaging in one approach throughout the experiment. Four recommendation strategies, including Random, Collaborative Filtering, Content-based Filtering, and Keyword-based Matching, are implemented and evaluated. The experimental results reveal that although all three HCI approaches exacerbate the filter bubble effect, a strategically designed combination of certain recommendation algorithms and HCI approaches has the potential to promote a more diverse and balanced online information environment.
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Yuan, Z., Li, W., Bai, Q. (2023). An Assessment of the Influence of Interaction and Recommendation Approaches on the Formation of Information Filter Bubbles. In: Wu, S., Yang, W., Amin, M.B., Kang, BH., Xu, G. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2023. Lecture Notes in Computer Science(), vol 14317. Springer, Singapore. https://doi.org/10.1007/978-981-99-7855-7_8
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DOI: https://doi.org/10.1007/978-981-99-7855-7_8
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