  Nowadays, most online services are hosted on multi-stakeholder marketplaces,
where consumers and producers may have different objectives. Conventional
recommendation systems, however, mainly focus on maximizing consumers'
satisfaction by recommending the most relevant items to each individual. This
may result in unfair exposure of items, thus jeopardizing producer benefits.
Additionally, they do not care whether consumers from diverse demographic
groups are equally satisfied. To address these limitations, we propose a
multi-objective optimization framework for fairness-aware recommendation,
Multi-FR, that adaptively balances accuracy and fairness for various
stakeholders with Pareto optimality guarantee. We first propose four fairness
constraints on consumers and producers. In order to train the whole framework
in an end-to-end way, we utilize the smooth rank and stochastic ranking policy
to make these fairness criteria differentiable and friendly to
back-propagation. Then, we adopt the multiple gradient descent algorithm to
generate a Pareto set of solutions, from which the most appropriate one is
selected by the Least Misery Strategy. The experimental results demonstrate
that Multi-FR largely improves recommendation fairness on multiple stakeholders
over the state-of-the-art approaches while maintaining almost the same
recommendation accuracy. The training efficiency study confirms our model's
ability to simultaneously optimize different fairness constraints for many
stakeholders efficiently.
