A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

Abstract

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end-user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increasing aggregate diversity in order to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high quality items that have low visibility or items from suppliers with low exposure to the users' final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

arXiv commentarXiv admin note: substantial text overlap with arXiv:2005.01148

References in corpus (13)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Fairness of Exposure in Rankings
• FA*IR: A Fair Top-k Ranking Algorithm
• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
• Equity of Attention: Amortizing Individual Fairness in Rankings
• Beyond Personalization: Research Directions in Multistakeholder Recommendation
• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
• Bias and Debias in Recommender System: A Survey and Future Directions
• Beyond Parity: Fairness Objectives for Collaborative Filtering
• Deconvolving Feedback Loops in Recommender Systems
• Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
• Challenging the Long Tail Recommendation

Cited by in corpus (9)

• A Survey on the Fairness of Recommender Systems
• Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
• Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
• A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems
• Intersectional Two-sided Fairness in Recommendation
• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
• Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
• Searching Personal Collections