Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

TL;DR

This paper introduces Multi-FR, a multi-objective optimization framework that balances accuracy and fairness in recommendation systems for multiple stakeholders, ensuring fair exposure and satisfaction across diverse groups.

Contribution

The paper proposes a novel end-to-end framework with differentiable fairness constraints and Pareto optimization for multi-stakeholder fairness-aware recommendation.

Findings

Significantly improves fairness across stakeholders.

Maintains comparable recommendation accuracy.

Efficiently optimizes multiple fairness constraints.

Abstract

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.