Location : Mitigating the “filter bubble” effect i...
题目：Mitigating the “filter bubble” effect in modern recommender systems
时间：2025年7月15日（周二）14:00-15:30
地点：中科院数学院南楼N218
报告人：Shiqing Wu（City University of Macau, Macau SAR）
报告摘要：
The 'filter bubble' effect has become a significant challenge in modern recommender systems, where personalized algorithms inadvertently confine users within narrow information ecosystems, reinforcing existing beliefs and limiting exposure to diverse perspectives. Consequently, users become progressively isolated from alternative viewpoints and conflicting information, posing significant threats to social cohesion, economic development, and individual intellectual development. The challenge is further complicated by the feedback loop inherent in recommendation systems, where user interactions continuously reinforce narrow preferences, making filter bubbles increasingly entrenched over time. An intuitive solution is to increase recommendation diversity. However, prioritizing content diversity often compromises recommendation accuracy, sacrificing user satisfaction and experience. This talk will share our research findings on mitigating the 'filter bubble' effect while maintaining user experience through novel approaches. Rather than forcing users to accept diverse recommendations, our methods guide them toward broader information exploration, demonstrating effective results in expanding user interests while preserving user satisfaction.
报告人简介：
Dr. Shiqing Wu is an Assistant Professor in the Faculty of Data Science at City University of Macau, Macau SAR, China. Before that, he was a Postdoctoral Research Fellow in the School of Computer Science at the University of Technology Sydney, Australia, from 2022 to 2024. He received his PhD in Computer Science from the University of Tasmania, Australia, in 2022, and a joint B.Sc. in Computer Science from Auckland University of Technology, New Zealand, and China Jiliang University, China, in 2016. His research interests include agent-based modeling, social influence analysis, recommender systems, and reinforcement learning. He has published research papers in prestigious venues, including conferences (e.g., SIGIR, ICDM, IJCAI, AAMAS) and journals (e.g., TKDE, TOIS). Shiqing also serves as a regular Program Committee Member for conferences such as IJCAI, KDD, ACM Multimedia, ECAI, and PRICAI. He is an Editorial Board Member of Information Processing & Management.
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