Fairness in Recommender Systems: Research Landscape and Future Directions

Abstract

Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

References in corpus (12)

• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
• A systematic review and taxonomy of explanations in decision support and recommender systems
• User-oriented Fairness in Recommendation
• Towards Long-term Fairness in Recommendation
• User-centered Evaluation of Popularity Bias in Recommender Systems
• Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
• This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
• Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
• Fairness and Transparency in Recommendation: The Users' Perspective
• Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis
• CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
• Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Cited by in corpus (23)

• A Survey on Popularity Bias in Recommender Systems
• A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
• A Survey on Intent-aware Recommender Systems
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Economic Recommender Systems -- A Systematic Review
• Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
• Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation
• Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
• 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
• Causality-Inspired Fair Representation Learning for Multimodal Recommendation
• Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms
• Stairway to Fairness: Connecting Group and Individual Fairness
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• External Evaluation of Discrimination Mitigation Efforts in Meta's Ad Delivery
• Improving Recommendation Fairness via Graph Structure and Representation Augmentation
• A Reproducibility Study of Product-side Fairness in Bundle Recommendation
• A Multistakeholder Approach to Value-Driven Co-Design of Recommender System Evaluation Metrics in Digital Archives
• User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
• Spectral Biclustering-Driven Scalability for Post-Hoc Explainability in Recommender Systems
• Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach
• Searching Personal Collections
• On Inherited Popularity Bias in Cold-Start Item Recommendation