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

TL;DR

This paper reviews fairness, bias, threats, and privacy issues in recommender systems, highlighting current challenges and proposing future research directions to enhance system robustness, fairness, and user trust.

Contribution

It provides a comprehensive analysis of biases, threats, and privacy concerns in recommender systems and suggests future research to address these critical issues.

Findings

Bias can lead to unfair treatment of user and item groups.

Threats such as attacks compromise system integrity.

Privacy issues require advanced protection mechanisms.

Abstract

Recommender systems are essential for personalizing digital experiences on e-commerce sites, streaming services, and social media platforms. While these systems are necessary for modern digital interactions, they face fairness, bias, threats, and privacy challenges. Bias in recommender systems can result in unfair treatment of specific users and item groups, and fairness concerns demand that recommendations be equitable for all users and items. These systems are also vulnerable to various threats that compromise reliability and security. Furthermore, privacy issues arise from the extensive use of personal data, making it crucial to have robust protection mechanisms to safeguard user information. This study explores fairness, bias, threats, and privacy in recommender systems. It examines how algorithmic decisions can unintentionally reinforce biases or marginalize specific user and item groups, emphasizing the need for fair recommendation strategies. The study also looks at the range of threats in the form of attacks that can undermine system integrity and discusses advanced privacy-preserving techniques. By addressing these critical areas, the study highlights current limitations and suggests future research directions to improve recommender systems' robustness, fairness, and privacy. Ultimately, this research aims to help develop more trustworthy and ethical recommender systems that better serve diverse user populations.