Robust Recommender System: A Survey and Future Directions

doi:10.1145/3757057

CSpace

	Robust Recommender System: A Survey and Future Directions
	Zhang, Kaike 1,2; Cao, Qi 1; Sun, Fei 1; Wu, Yunfan 1; Tao, Shuchang 1; Shen, Huawei 1,2; Cheng, Xueqi 1,2
	2026
发表期刊	ACM COMPUTING SURVEYS
ISSN	0360-0300
卷号	58 期号:1 页码:38
摘要	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 robustness of recommender systems against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on robust recommender systems. 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.
关键词	Recommender system robustness attack noise defense denoise
DOI	10.1145/3757057
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:001607381000003
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42974
专题	中国科学院计算技术研究所
通讯作者	Zhang, Kaike
作者单位	1.Chinese Acad Sci, State Key Lab AI Safety, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Kaike,Cao, Qi,Sun, Fei,et al. Robust Recommender System: A Survey and Future Directions[J]. ACM COMPUTING SURVEYS,2026,58(1):38.
APA	Zhang, Kaike.,Cao, Qi.,Sun, Fei.,Wu, Yunfan.,Tao, Shuchang.,...&Cheng, Xueqi.(2026).Robust Recommender System: A Survey and Future Directions.ACM COMPUTING SURVEYS,58(1),38.
MLA	Zhang, Kaike,et al."Robust Recommender System: A Survey and Future Directions".ACM COMPUTING SURVEYS 58.1(2026):38.