Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach

TL;DR

This paper introduces a Nash Social Welfare approach to balance fairness and match rates in reciprocal recommender systems, addressing unfairness issues while maintaining high matching efficiency.

Contribution

It proposes the NSW method and its generalization, the alpha-SW method, along with an efficient approximation algorithm, to improve fairness without sacrificing match rates.

Findings

NSW method achieves nearly envy-free recommendations.

Alpha-SW balances fairness and match rates effectively.

Approach is validated on synthetic and real datasets.

Abstract

Matching platforms, such as online dating services and job recommendations, have become increasingly prevalent. For the success of these platforms, it is crucial to design reciprocal recommender systems (RRSs) that not only increase the total number of matches but also avoid creating unfairness among users. In this paper, we investigate the fairness of RRSs on matching platforms. From the perspective of fair division, we define the users' opportunities to be recommended and establish the fairness concept of envy-freeness in the allocation of these opportunities. We first introduce the Social Welfare (SW) method, which approximately maximizes the number of matches, and show that it leads to significant unfairness in recommendation opportunities, illustrating the trade-off between fairness and match rates. To address this challenge, we propose the Nash Social Welfare (NSW) method, which alternately optimizes two NSW functions and achieves nearly envy-free recommendations. We further generalize the SW and NSW method to the $\alpha$-SW method, which balances the trade-off between fairness and high match rates. Additionally, we develop a computationally efficient approximation algorithm for the SW/NSW/$\alpha$-SW methods based on the Sinkhorn algorithm. Through extensive experiments on both synthetic datasets and two real-world datasets, we demonstrate the practical effectiveness of our approach.