In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, conversely, aiding providers in marketing products to interested users. The growing awareness of discrimination in machine learning methods has recently motivated both academia and industry to research how fairness can be ensured in recommender systems. For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes. In particular, consumer-side fairness, which focuses on mitigating discrimination experienced by users of recommender systems, has seen a vast number of diverse approaches for addressing different types of discrimination. The nature of said discrimination depends on the setting and the applied fairness interpretation, of which there are many variations. This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems. To that end, a novel taxonomy based on high-level fairness interpretation is proposed and used to categorize the research and their proposed fairness evaluation metrics. Finally, we highlight some suggestions for the future direction of the field.

翻译：在日益增长的数字化程度背景下，我们面临着与可扩展性相关的主要挑战。推荐系统已成为不可或缺的工具，既帮助用户浏览不断增长的数据量，又反过来协助提供商向感兴趣的用户营销产品。近年来，对机器学习方法中歧视现象日益增长的认识，促使学术界和工业界研究如何在推荐系统中确保公平性。职业推荐便是一个典型例子：历史数据中的偏差可能导致推荐系统将某一性别与较低薪资联系起来，或助长刻板印象的传播。特别是消费者端公平性，它侧重于减轻推荐系统用户所经历的歧视，目前已涌现出大量针对不同类型歧视的多样化处理方法。这些歧视的性质取决于具体场景和所应用的公平性解释，而公平性解释本身存在多种变体。本综述旨在系统概述并讨论当前推荐系统中消费者端公平性的研究现状。为此，我们提出了一种基于高层级公平性解释的新型分类体系，并用于对现有研究及其提出的公平性评估指标进行分类。最后，我们为该领域的未来发展方向提出了一些建议。