Title: Balancing Fairness and High Match Rates in Reciprocal Recommender Systems: A Nash Social Welfare Approach Abstract: Matching platforms, such as online dating services that help users find partners and job recommendations that connect job seekers with employers, have become increasingly prevalent. For the success of these matching platforms, it is crucial to design reciprocal recommender systems that not only increase the total number of matches but also avoid creating unfairness among users. In this paper, we investigate the fairness of reciprocal recommender systems 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 […]