Towards Fair Recommendation in Two-Sided Platforms

Abstract

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. 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 reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed {\em FairRec} algorithm guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of {\em FairRec} in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

arXiv commentACM Transactions on the Web, Volume 16, Issue 2 May 2022, Article no 8. arXiv admin note: substantial text overlap with arXiv:2002.10764

References in corpus (7)

• Fairness of Exposure in Rankings
• Translation-based Recommendation
• Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
• Estimating Position Bias without Intrusive Interventions
• Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness

Cited by in corpus (5)

• Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation
• Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
• Vertical Allocation-based Fair Exposure Amortizing in Ranking
• Subgroup Fairness in Two-Sided Markets
• Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach