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.

中文翻译：

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HyperFair：一种集成公平标准的软方法

推荐系统正被用于越来越多样化的领域，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，这是一种在混合推荐系统中强制执行软公平约束的通用框架。HyperFair 模型将公平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，并表明它特别适合这项任务，因为它具有表现力，并且可以以简洁和可解释的方式将结构约束添加到系统中。我们提出了两种使用我们介绍的方法的方法：首先作为概率软逻辑推荐系统模板的扩展；其次是一种公平的改造技术，可用于提高黑盒模型预测的公平性。我们通过实施多个 HyperFair 混合推荐器来凭经验验证我们的方法，并将它们与最先进的公平推荐器进行比较。我们还进行了实验，展示了我们的方法在改造黑盒模型的任务中的有效性以及强制执行的公平性和预测性能之间的权衡。