HyperFair：一种整合公平标准的软方法
HyperFair: A Soft Approach to Integrating Fairness Criteria
摘要：
推荐系统正在越来越多的领域中使用，这些领域可能会产生重大的社会和个人影响。因此，考虑公平性是设计和评估此类系统的关键步骤。在本文中，我们介绍了HyperFair，一个通用的框架，用于执行软公平约束的混合推荐系统。HyperFair模型将公平性度量的变化集成为联合推理目标函数的正则化。我们使用概率软逻辑实现我们的方法，并表明它是特别适合这项任务，因为它是表达和结构约束可以添加到系统中的一个简洁和可解释的方式。我们提出了两种方法来采用我们介绍的方法：第一，作为概率软逻辑推荐系统模板的扩展;第二，作为一个公平的改装技术，可以用来提高从黑盒模型预测的公平性。我们通过实施多个HyperFair混合推荐器并将其与最先进的公平推荐器进行比较，以经验验证我们的方法。我们还进行了实验，显示了我们的方法对黑盒模型改造任务的有效性，以及执行的公平性和预测性能之间的权衡。
英文摘要：
Recommender systems are being employed across an increasingly diverse set of domains that can potentially make a significant social and individual impact. For this reason, considering fairness is a critical step in the design and evaluation of such systems. In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system. HyperFair models integrate variations of fairness metrics as a regularization of a joint inference objective function. We implement our approach using probabilistic soft logic and show that it is particularly well-suited for this task as it is expressive and structural constraints can be added to the system in a concise and interpretable manner. We propose two ways to employ the methods we introduce: first as an extension of a probabilistic soft logic recommender system template; second as a fair retrofitting technique that can be used to improve the fairness of predictions from a black-box model. We empirically validate our approach by implementing multiple HyperFair hybrid recommenders and compare them to a state-of-the-art fair recommender. We also run experiments showing the effectiveness of our methods for the task of retrofitting a black-box model and the trade-off between the amount of fairness enforced and the prediction performance.
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。
