CaDRec: Contextualized and Debiased Recommender Model - Fairness in RecSys

Explore a 13-minute conference talk from the Association for Computing Machinery (ACM) that delves into the innovative CaDRec model, a contextualized and debiased recommender system. Learn about the latest advancements in fairness for recommender systems as authors Xinfeng Wang, Fumiyo Fukumoto, Jin Cui, Yoshimi Suzuki, Jiyi Li, and Dongjin Yu present their research findings. Gain insights into how the CaDRec model addresses bias in recommendation algorithms while incorporating contextual information to improve the accuracy and fairness of recommendations. Understand the potential impact of this research on creating more equitable and effective recommender systems across various applications.

SIGIR 2024 M1.7 [fp] CaDRec: Contextualized and Debiased Recommender Model