A Survey on the Fairness of Recommender Systems

Abstract

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

Submitted to the Special Section on Trustworthy Recommendation and Search of ACM TOIS on March 27, 2022 and accepted on June 6

References in corpus (10)

• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
• User-oriented Fairness in Recommendation
• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• Towards Long-term Fairness in Recommendation
• Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
• SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios
• A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
• Maximizing Marginal Fairness for Dynamic Learning to Rank
• Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
• TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

Cited by in corpus (31)

• Recommender Systems in the Era of Large Language Models (LLMs)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends
• Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey
• Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
• Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
• SPRec: Self-Play to Debias LLM-based Recommendation
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Intersectional Two-sided Fairness in Recommendation
• Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
• Rethinking the filter bubble? Developing a research agenda for the protective filter bubble
• It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
• Uncovering Bias in Personal Informatics
• Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning
• Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
• Recommending With, Not For: Co-Designing Recommender Systems for Social Good
• Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
• Understanding Fairness in Recommender Systems: A Healthcare Perspective
• Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness
• CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
• Causality-Inspired Fair Representation Learning for Multimodal Recommendation
• Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
• Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
• Stairway to Fairness: Connecting Group and Individual Fairness
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• Impression-Aware Recommender Systems
• Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems
• Countering Mainstream Bias via End-to-End Adaptive Local Learning
• Improving Recommendation Fairness via Graph Structure and Representation Augmentation
• A Reproducibility Study of Product-side Fairness in Bundle Recommendation
• User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
• Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier