Location : Nudging Toward Responsible Recommendation
题目：Nudging Toward Responsible Recommendation
时间：2025年1月15日（周三）14:00-16:00
地点：中科院数学院南楼N212
报告人：Quan Bai（University of Tasmania）
报告摘要：
Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. In this talk, I will first introduce the impact of AI in the generation of belief isolation, and concept of responsible recommendation. Furthermore, I will introduce some Agent-based and Generative AI-empowered Responsible Recommendation approaches, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as an intermediate agency between users and existing preference-based recommendation systems, these approaches can achieve responsible recommendation without sacrificing too much usability.
报告人简介：
Associate Professor Quan Bai received his PhD (2007) and MSc (2002) from the University of Wollongong, Australia. After he received his PhD, Quan worked as a Postdoctoral Research Follow for the University of Wollongong (2007-2009), and for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (2009-2011). In May 2011, Associate Professor Quan Bai joined the School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology as a lecturer, and then in 2012, promoted to a senior lecturer. Since March 2019, Quan Bai has been with the School of ICT, University of Tasmania, as an associate professor.
Quan Bai is a distinguished expert in agent-based modelling and multi-agent coordination, is at the forefront of cutting-edge research. His work is cantered on the application of advanced AI methodologies to model intricate systems comprising numerous complex and interdependent components. Driven by the goal of orchestrating self-interested agents towards optimal outcomes, he has a remarkable record of over 150 high-quality publications in related fields and has secured research funding exceeding 1.6 million AUDs, including prestigious NHMRC grants.
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