用社会选择理论融合多种公平标准，提升推荐系统对多元利益相关者的需求响应。
Social Choice for Heterogeneous Fairness in Recommendation
- 引入多代理框架，将不同公平定义视为利益相关者偏好
- 在多个数据集上实现跨维度的异质公平整合
- 适合关注多方公平性权衡的推荐系统研究者
推荐系统中的算法公平性需关注具有竞争性需求的多样化利益相关者。以往工作常受限于固定的单目标公平定义，这些定义被嵌入算法或优化准则中，仅适用于单一公平维度，或在各维度上采取相同处理方式。此类狭隘的概念化限制了公平感知解决方案对实际中多样利益相关者需求和公平定义的适应能力。本文从计算社会选择的角度出发，采用多代理框架，探索不同社会选择机制的特性，并成功实现了在多个数据集上对多种异质公平定义的整合。
原文摘要 · Abstract (English)
Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets.
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