Robust Recommender System: A Survey and Future Directions
文献类型：期刊论文
| 作者 | Zhang, Kaike1,2; Cao, Qi2; Sun, Fei2; Wu, Yunfan2; Tao, Shuchang2; Shen, Huawei1,2; Cheng, Xueqi1,2 |
| 刊名 | ACM COMPUTING SURVEYS
 |
| 出版日期 | 2026 |
| 卷号 | 58期号:1页码:38 |
| 关键词 | Recommender system robustness attack noise defense denoise |
| ISSN号 | 0360-0300 |
| DOI | 10.1145/3757057 |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001607381000003 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42973]  |
| 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.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab AI Safety, Inst Comp Technol, 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. |
入库方式： OAI收割
来源：计算技术研究所
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