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

Falguni Roy, Xiaofeng Ding, K. -K. R. Choo, Pan Zhou

TL;DR

This study explores fairness, bias, threats, and privacy in recommender systems and examines how algorithmic decisions can unintentionally reinforce biases or marginalize specific user and item groups, emphasizing the need for fair recommendation strategies.

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.

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

TL;DR

This study explores fairness, bias, threats, and privacy in recommender systems and examines how algorithmic decisions can unintentionally reinforce biases or marginalize specific user and item groups, emphasizing the need for fair recommendation strategies.

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.
Paper Structure (28 sections, 1 equation, 4 figures, 7 tables)

This paper contains 28 sections, 1 equation, 4 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Existing Surveys and Current Study Contribution
  3. Contributions of This Survey
  4. Paper Collection Methodology
  5. Scope and Study Structure
  6. Fairness of Recommender System
  7. Bias in Recommender System
  8. Threats of Recommender System
  9. User-Oriented Attacks
  10. Re-identification Attack (RA)
  11. Inference Attack (IA)
  12. Secret Sharer Attack
  13. Item-Oriented Attacks
  14. Shilling Attack (Profile Injection Attack/Data Poisoning Attack)
  15. Adversarial Attack
  16. ...and 13 more sections

Figures (4)

  • Figure 2: Classification of Fairness of Recommender system
  • Figure 3: Taxonomy of Bias in Recommender system
  • Figure 4: Recommender System Attack taxonomy
  • Figure 5: A Taxonomy of Privacy-Preserving Recommender System (PPRS)