Two-sided fairness in rankings via Lorenz dominance

Abstract

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.

References in corpus (5)

• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• Multisided Fairness for Recommendation
• Auditing Search Engines for Differential Satisfaction Across Demographics
• TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers
• A Framework for Fairness in Two-Sided Marketplaces