Two-sided fairness in rankings via Lorenz dominance
This addresses fairness in recommender systems for both users and producers, offering a novel method that is not incremental but provides a new paradigm for fairness.
The paper tackles the problem of generating rankings that are fair to both users and item producers in recommender systems, using Lorenz efficiency to increase utility for worse-off individuals, and shows that their approach always produces fair rankings with increased utility for the worse-off at lower overall utility costs.
We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or movies) and reciprocal recommendation (e.g., dating). Following concepts of distributive justice in welfare economics, our notion of fairness aims at increasing the utility of the worse-off individuals, which we formalize using the criterion of Lorenz efficiency. It guarantees that rankings are Pareto efficient, and that they maximally redistribute utility from better-off to worse-off, at a given level of overall utility. We propose to generate rankings by maximizing concave welfare functions, and develop an efficient inference procedure based on the Frank-Wolfe algorithm. We prove that unlike existing approaches based on fairness constraints, our approach always produces fair rankings. Our experiments also show that it increases the utility of the worse-off at lower costs in terms of overall utility.