With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters ``dirty'' data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training for defending against malicious attacks, and regularization, purification, self-supervised learning for defending against malicious attacks. Additionally, we summarize evaluation metrics and commonly used datasets for assessing robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to provide readers with a comprehensive understanding of robust recommender systems and to identify key pathways for future research and development. To facilitate ongoing exploration, we maintain a continuously updated GitHub repository with related research: https://github.com/Kaike-Zhang/Robust-Recommender-System.

翻译：随着信息的快速增长，推荐系统已成为提供个性化建议和克服信息过载不可或缺的工具。然而，在实际部署中，推荐系统常常面临"脏"数据问题，其中噪声或恶意信息可能导致异常推荐。因此，提高推荐系统对此类脏数据的鲁棒性研究已引起广泛关注。本综述全面回顾了关于推荐系统鲁棒性的近期工作。我们首先提出一个分类法来组织当前抵御恶意攻击和自然噪声的技术。随后，我们探讨了各类别中的前沿方法，包括用于防御恶意攻击的欺诈者检测、对抗训练、可证明鲁棒训练，以及用于防御恶意攻击的正则化、净化和自监督学习。此外，我们总结了用于评估鲁棒性的评价指标和常用数据集。我们讨论了不同推荐场景下的鲁棒性及其与准确性、可解释性、隐私性和公平性等其他属性的相互作用。最后，我们深入探讨了这一新兴领域的开放问题和未来研究方向。我们的目标是为读者提供对鲁棒推荐系统的全面理解，并指明未来研究和发展的关键路径。为促进持续探索，我们维护了一个持续更新的GitHub仓库，汇集了相关研究：https://github.com/Kaike-Zhang/Robust-Recommender-System。