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 against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess 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 equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development.

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的重要组成部分。然而，在实际部署中，推荐系统常常面临“脏数据”的挑战，其中噪声或恶意信息可能导致异常推荐。因此，提升推荐系统对此类脏数据的鲁棒性研究引起了广泛关注。本综述全面回顾了推荐系统鲁棒性的最新工作。我们首先提出一个分类体系，以组织当前抵御恶意攻击和自然噪声的技术。随后，我们深入探讨了每个类别中的最新方法，包括欺诈者检测、对抗训练、可验证鲁棒训练（针对恶意攻击），以及正则化、净化、自监督学习（针对自然噪声）。此外，我们总结了评估鲁棒性所使用的评价指标和常用数据集。我们还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之间的相互作用。最后，我们探讨了这一新兴领域中的开放问题与未来研究方向。我们的目标是让读者全面理解鲁棒推荐系统，并为未来的研究和发展指明方向。