A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research

Abstract

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 erode reliability, and extensive use of personal data creates privacy risks that demand robust safeguards. This study surveys these challenges, shows how algorithmic choices can unintentionally reinforce disparities, and reviews strategies for fair recommendation and privacy-preserving learning. We frame these aspects with a unified Privacy–Accuracy–Fairness plus Threat framework that spans data retrieval, ranking and serving, and we advocate standardized evaluation on fixed candidate item pools to isolate tradeoffs between quality and responsibility metrics. By integrating these dimensions, we synthesize current limitations and outline future directions to improve threat resilience, fairness and privacy. We also situate the discussion in ethical and regulatory contexts and translate those principles into practical controls that foster trustworthy systems for diverse populations.