FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

Abstract

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.

In Proceedings of The Web Conference (WWW) 2020

References in corpus (2)

• Translation-based Recommendation
• Estimating Position Bias without Intrusive Interventions

Cited by in corpus (28)

• A Survey on the Fairness of Recommender Systems
• User-oriented Fairness in Recommendation
• Fairness in Recommender Systems: Research Landscape and Future Directions
• Personalized Counterfactual Fairness in Recommendation
• A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems
• A Stakeholder-Centered View on Fairness in Music Recommender Systems
• Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
• Towards Fair Recommendation in Two-Sided Platforms
• Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
• Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
• Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective
• Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation
• Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks
• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• User Tampering in Reinforcement Learning Recommender Systems
• Intersectional Two-sided Fairness in Recommendation
• Curse of "Low" Dimensionality in Recommender Systems
• Vertical Allocation-based Fair Exposure Amortizing in Ranking
• Subgroup Fairness in Two-Sided Markets
• FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms
• Towards Fair Allocation in Social Commerce Platforms
• Group fairness without demographics using social networks
• Stairway to Fairness: Connecting Group and Individual Fairness
• Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
• PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
• Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach
• Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
• Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach