Towards Fair Recommendation in Two-Sided Platforms

TL;DR

This paper introduces FairRec, an algorithm that balances fairness for both producers and customers in two-sided recommendation platforms, ensuring fair exposure and satisfaction while maintaining recommendation quality.

Contribution

The paper proposes a novel fair recommendation algorithm, FairRec, that guarantees fairness for both producers and customers in two-sided markets, with an extension FairRecPlus for improved performance.

Findings

FairRec achieves two-sided fairness with minimal loss in recommendation quality.

Extensive experiments validate the effectiveness of FairRec on real-world datasets.

FairRecPlus enhances recommendation performance while maintaining fairness guarantees.

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.