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A flexible framework for evaluating user and item fairness in recommender systems

机译：灵活的框架，用于评估推荐系统中的用户和项目公平性

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One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment-meaning they ignore the information about users' memberships in protected classes during training-or in impact-by enforcing proportional beneficial outcomes to users in different protected classes. In the recommender systems community, fairness has been studied with respect to both users' and items' memberships in protected classes defined by some sensitive attributes (e.g., gender or race for users, revenue in a multi-stakeholder setting for items). Again here, the concept has been commonly interpreted as some form of equality-i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this work, we propose a probabilistic framework based on generalized cross entropy (GCE) to measure fairness of a given recommendation model. The framework comes with a suite of advantages: first, it allows the system designer to define and measure fairness for both users and items and can be applied to any classification task; second, it can incorporate various notions of fairness as it does not rely on specific and predefined probability distributions and they can be defined at design time; finally, in its design it uses a gain factor, which can be flexibly defined to contemplate different accuracy-related metrics to measure fairness upon decision-support metrics (e.g., precision, recall) or rank-based measures (e.g., NDCG, MAP). An experimental evaluation on four real-world datasets shows the nuances captured by our proposed metric regarding fairness on different user and item attributes, where nearest-neighbor recommenders tend to obtain good results under equality constraints. We observed that when the users are clustered based on both their interaction with the system and other sensitive attributes, such as age or gender, algorithms with similar performance values get different behaviors with respect to user fairness due to the different way they process data for each user cluster.
机译：研究工作的一个共同的特征，专注于公平评估（在机器学习中）是他们在治疗中呼吁某种形式的奇偶校验（平等），这意味着他们在训练期间忽略了有关用户在受保护的类别中的有关用户的信息的信息 - 通过对不同受保护类的用户执行比例有益结果。在推荐系统社区中，已经研究了由某些敏感属性定义的受保护类别的用户和物品的成员资格（例如，用于用户的性别或种族，物品的多利益相关者环境中的收入）中的受保护类别中的用户的成员。这里，该概念通常被解释为某种形式的平等-i。，系统在符合其所有用户的信息需求的程度。在这项工作中，我们提出了一种基于广义交叉熵（GCE）的概率框架来测量给定推荐模型的公平性。该框架配有一套优势：首先，它允许系统设计师定义和测量用户和项目的公平，可以应用于任何分类任务;其次，它可以包含各种公平的概念，因为它不依赖于特定和预定义的概率分布，它们可以在设计时定义;最后，在其设计中，它使用增益因子，这可以灵活地定义，以考虑不同的精度相关的指标，以测量决策支持度量（例如，精确，召回）或基于秩的措施（例如，NDCG，MAP）的公平性。四个现实世界数据集的实验评估显示了我们拟议的指标对不同用户和项目属性的公平捕获的细微差异，最近邻的推荐人倾向于在平等限制下获得良好的结果。我们观察到，当用户基于它们与系统的交互和其他敏感属性的互动，例如年龄或性别，具有相似性能值的算法，由于它们为每个处理数据的不同方式获得了不同的行为用户群集。

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