As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization. This fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. For example, products that were once popular may become no longer popular, and vice versa. As a result, the system that aims to maintain long-term fairness on the item exposure in different popularity groups must accommodate this change in a timely fashion. Novel to this work, we explore the problem of long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process. We tackle this problem by proposing a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes. Experiments on several real-world datasets verify our framework's superiority in terms of recommendation performance, short-term fairness, and long-term fairness.

中文翻译：

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争取长期公平的推荐

随着推荐系统（RS）在日常生活中影响越来越多的人，推荐中的公平性问题变得越来越重要。现有的大多数关于公平意识建议的方法都位于静态或一次性设置中，其中受保护的项目组是固定的，并且该模型基于公平约束的优化提供了一次性的公平解决方案。这没有考虑推荐系统的动态性质，在该系统中，诸如项目受欢迎度之类的属性可能会由于推荐策略和用户参与而随时间变化。例如，曾经受欢迎的产品可能不再受欢迎，反之亦然。结果，旨在保持不同人气群体中的商品展示长期公平的系统必须及时适应这种变化。这项工作很新颖，我们探讨了推荐中的长期公平问题，并通过动态公平学习解决了这一问题。我们专注于不同组中项目曝光的公平性，而组的划分基于项目受欢迎程度，在推荐过程中，项目受欢迎程度会随着时间动态变化。为了解决这个问题，我们提出了一种推荐的公平约束强化学习算法，该算法将推荐问题建模为约束马尔可夫决策过程（CMDP），以便模型可以动态调整其推荐策略以确保始终满足公平要求当环境变化时。在多个实际数据集上进行的实验证明了我们的框架在推荐效果，短期公平性，