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
Recommender systems personalize digital experiences across e-commerce, streaming, and social media, yet they face linked challenges in fairness, bias, threats, and privacy. Bias can lead to unequal treatment, so fairness seeks equitable recommendations for all stakeholders. At the same time, adversarial attacks can ero...