Review
Open access
Sep 2024
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
This study shows how algorithmic choices can unintentionally reinforce disparities, and reviews strategies for fair recommendation and privacy-preserving learning, and synthesizes current limitations and outline future directions to improve threat resilience, fairness and privacy.
Falguni Roy, Xiaofeng Ding, Kim-Kwang Raymond Choo et al.
· ACM Computing Surveys · 4 citations