Adaptive Fair Representation Learning for Personalized Fairness in Recommendations - Lecture 7

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.

SIGIR 2024 M1.7 [fp] Adaptive Fair Representation Learning for Personalized Fairness