Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues such as data sparsity can also degrade the performance of recommender systems. Therefore, enhancing the robustness of recommender systems has become an increasingly important research topic. In this survey, we provide a comprehensive overview of the robustness of recommender systems. Based on our investigation, we categorize the robustness of recommender systems into adversarial robustness and non-adversarial robustness. In the adversarial robustness, we introduce the fundamental principles and classical methods of recommender system adversarial attacks and defenses. In the non-adversarial robustness, we analyze non-adversarial robustness from the perspectives of data sparsity, natural noise, and data imbalance. Additionally, we summarize commonly used datasets and evaluation metrics for evaluating the robustness of recommender systems. Finally, we also discuss the current challenges in the field of recommender system robustness and potential future research directions. Additionally, to facilitate fair and efficient evaluation of attack and defense methods in adversarial robustness, we propose an adversarial robustness evaluation library--ShillingREC, and we conduct evaluations of basic attack models and recommendation models. ShillingREC project is released at https://github.com/chengleileilei/ShillingREC.

翻译：近年来，推荐系统取得了显著成功。然而，由于推荐系统的开放性，它们仍易受恶意攻击。此外，训练数据中的自然噪声以及数据稀疏性等问题也会降低推荐系统的性能。因此，增强推荐系统的鲁棒性已成为日益重要的研究课题。在本综述中，我们全面概述了推荐系统的鲁棒性。基于我们的研究，我们将推荐系统的鲁棒性分为对抗鲁棒性和非对抗鲁棒性。在对抗鲁棒性方面，我们介绍了推荐系统对抗攻击与防御的基本原理和经典方法。在非对抗鲁棒性方面，我们从数据稀疏性、自然噪声和数据不平衡的角度分析了非对抗鲁棒性。此外，我们总结了评估推荐系统鲁棒性常用的数据集和评估指标。最后，我们还讨论了当前推荐系统鲁棒性领域面临的挑战以及未来潜在的研究方向。同时，为促进对抗鲁棒性中攻击与防御方法的公平高效评估，我们提出了一个对抗鲁棒性评估库——ShillingREC，并对基本攻击模型和推荐模型进行了评估。ShillingREC项目已发布在https://github.com/chengleileilei/ShillingREC。