  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.
