“胜利者是...“：用于多组公平感知推荐的动态彩票
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation
摘要：
随着推荐系统的设计和部署越来越多的社会后果的应用程序，它已成为重要的是要考虑这些系统的公平性。关于推荐的公平性已经有了相当多的研究。然而，我们认为，以前的文献一直是基于简单的，统一的，往往是一维的公平假设的概念，不承认现实世界的复杂性，公平意识的应用程序。在本文中，我们明确表示的设计决策，进入多个定义和交叉保护组的准确性和公平性之间的权衡，支持多个公平性指标。该框架还允许推荐器调整其性能的基础上，已交付的建议，在一个时间范围内的历史视图，动态重新平衡公平性的关注。在这个框架内，我们制定了基于彩票的公平性问题之间的选择机制，并证明了他们的性能在两个推荐域。
英文摘要：
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit. There has been considerable research on recommendation fairness. However, we argue that the previous literature has been based on simple, uniform and often uni-dimensional notions of fairness assumptions that do not recognize the real-world complexities of fairness-aware applications. In this paper, we explicitly represent the design decisions that enter into the trade-off between accuracy and fairness across multiply-defined and intersecting protected groups, supporting multiple fairness metrics. The framework also allows the recommender to adjust its performance based on the historical view of recommendations that have been delivered over a time horizon, dynamically rebalancing between fairness concerns. Within this framework, we formulate lottery-based mechanisms for choosing between fairness concerns, and demonstrate their performance in two recommendation domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
