With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this paper, we propose a fairness-aware recommendation algorithm (DALFRec) based on user-side and item-side adversarial learning to mitigate the effects of sensitive information in both sides of the recommendation process. Firstly, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.

随着推荐算法的发展，研究人员越来越关注推荐中的用户歧视等公平性问题。为了解决这些问题，现有的工作经常过滤用户的敏感信息，这些信息在学习用户表示的过程中可能会导致歧视。然而，这些方法忽略了项目内容属性和用户敏感信息之间的潜在关系。在本文中，我们提出了一种基于用户端和项目端对抗性学习的公平感知推荐算法（DALFRec），以减轻敏感信息在推荐过程中的影响。首先，我们进行统计分析来证明项目信息与用户敏感属性之间的潜在关系。然后，我们设计了一个双边对抗学习网络，在用户端和物品端同时过滤掉用户的敏感信息。此外，我们提出了一种新的评估策略，利用项目内容属性和用户敏感属性之间的潜在关系来更好地评估算法减少歧视的能力。我们对三个真实数据集的实验证明了我们提出的算法相对于最先进的方法的优越性。