WE-Rec: A fairness-aware reciprocal recommendation based on Walrasian equilibrium

Abstract

The emergence of online dating and recruiting platforms brings big challenges to the reciprocal recommendation which has attracted a lot of research attention. Most previous approaches improved the accuracy and diversity of reciprocal recommendations, but few researcher made efforts on the fairness-aware recommendation which aims to avoid the discrimination and mistreatment of vulnerable groups. In this paper, we concentrate on the research of fairness-aware recommendations in the reciprocal recommender system and propose an approach to rerank the recommendation list by optimizing three significant fairness-aware criteria between parties (i.e., buyers and sellers) based on Walrasian equilibrium: (1) the disparity of service; (2) the similarity of mutual preference; (3) the equilibrium of demand and supply. According to these definitions of fairness, we cast the task of reciprocal recommendation as a multi-objective optimization considering the satisfaction of individuals, the fairness of recommendations, and the market clearing simultaneously. The extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of our approach.