Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations. A social choice formulation of the fairness problem, operating within a multi-agent architecture of fairness concerns, offers a flexible and multi-aspect alternative to fairness-aware recommendation approaches. Leveraging social choice allows for increased generality and the possibility of tapping into well-studied social choice algorithms for resolving the tension between multiple, competing fairness concerns. This paper explores a range of options for choice mechanisms in multi-aspect fairness applications using both real and synthetic data and shows that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs. We also show that a multi-agent formulation offers flexibility in adapting to user population dynamics.

翻译：推荐系统中的公平性问题在实践中常具有复杂性，而简化研究框架难以充分捕捉这种复杂性。基于多智能体架构的公平性关切社会选择表述，为公平感知推荐方法提供了一种灵活且多方面的替代方案。借助社会选择理论可提升通用性，并利用经过充分研究的社会选择算法来化解多重竞争性公平关切之间的矛盾。本文通过真实数据和合成数据，在多维度公平性应用中探索了多种选择机制方案，结果表明不同类型的选择与分配机制会产生不同但一致的公平性/准确性权衡。我们还证明，多智能体表述能灵活适应用户群体动态变化。