Towards Fair Recommendation in Two-Sided Platforms

• URL: http://arxiv.org/abs/2201.01180v1
• Date: Sun, 26 Dec 2021 05:14:56 GMT
• Title: Towards Fair Recommendation in Two-Sided Platforms
• Authors: Arpita Biswas, Gourab K Patro, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty
• Abstract summary: We propose a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. 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.
• Score: 36.35034531426411
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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