Explore a conference talk on fair sequential recommendation systems that don't rely on user demographics. Delve into the research presented by authors Huimin Zeng, Zhankui He, Zhenrui Yue, Julian McAuley, and Dong Wang at the SIGIR 2024 conference. Learn about innovative approaches to ensuring fairness in recommendation systems without using potentially sensitive demographic information. Gain insights into the challenges and solutions in creating equitable recommendation algorithms that respect user privacy while still providing personalized suggestions. Understand the implications of this research for the future of recommendation systems in various applications and industries.
Fair Sequential Recommendation without User Demographics - SIGIR 2024
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
SIGIR 2024 M1.7 [fp] Fair Sequential Recommendation without User Demographics
Taught by
Association for Computing Machinery (ACM)
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