Adaptive Fair Representation Learning for Personalized Fairness in Recommendations - Lecture 7
Offered By: Association for Computing Machinery (ACM) via YouTube
Course Description
Overview
Explore an innovative approach to fairness in recommender systems through this 15-minute conference talk presented at SIGIR 2024. Delve into the concept of Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment, as discussed by authors Xinyu Zhu, Lilin Zhang, and Ning Yang. Gain insights into how this method addresses fairness challenges in personalized recommendations, potentially revolutionizing the way recommender systems balance user preferences with ethical considerations.
Syllabus
SIGIR 2024 M1.7 [fp] Adaptive Fair Representation Learning for Personalized Fairness
Taught by
Association for Computing Machinery (ACM)
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