The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Recommendation Unlearning
Open accessAbstract
Recommender systems provide essential web services by learning users’ personal preferences from collected data. However, in many cases, systems also need to forget some training data. From the perspective of privacy, users desire a tool to erase the impacts of their sensitive data from the trained models. From the perspective of utility, if a system’s utility is damaged by some bad data, the system needs to forget such data to regain utility. While unlearning is very important, it has not been well-considered in existing recommender systems. Although there are some researches have studied the problem of machine unlearning, existing methods can not be directly applied to recommendation as they are unable to consider the collaborative information.
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“The unlearning schemes proposed in References [29, 30, 33, 47, 68, 69] are essentially based on a retraining mechanism that naturally has a verifiability property.”
From Machine Unlearning: A Survey (2024) · cites this paper as [33]
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“Machine unlearning has played an essential role in many applications [33, 34].”
From Machine Unlearning: A Survey (2024) · cites this paper as [33]
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“Nevertheless, the above unlearning schemes [30, 33, 69] usually need to cache a large number of intermediate results to complete the unlearning process. This will consume a lot of storage space.”
From Machine Unlearning: A Survey (2024) · cites this paper as [33]
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Citations
Cites
Community structure in social and biological networks
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Matrix Factorization Techniques for Recommender Systems
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Neural Collaborative Filtering
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Neural Graph Collaborative Filtering
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Graph Convolutional Neural Networks for Web-Scale Recommender Systems
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Research works printed but not linked: 11
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- Gert Cauwenberghs and Tomaso Poggio. 2001. Incremental and Decremental Support Vector Machine Learning. In Advances in Neural Information Processing Systems, Vol. 13. MIT Press, 388–394.
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- Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2021. Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. In Proceedings of 32nd International Conference on Algorithmic Learning Theory, Vol. 132. PMLR, 931–962.
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Cited by
Sorted by citations
A Survey of Machine Unlearning
Nguyên, T. T., Huynh, T. T., Ren, Z. X., Nguyen, P. L., Liew, A. W., Yin, H., & Nguyen, Q. V. H. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3749987 · Cited by 540
Recent advances on federated learning: A systematic survey
Liu, B., Lv, N., Guo, Y., & Li, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128019 · Cited by 275
Fast Yet Effective Machine Unlearning
Tarun, A. K., Chundawat, V. S., Mandal, M., & Kankanhalli, M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3266233 · Cited by 203
Fast Yet Effective Machine Unlearning
Tarun, A. K., Chundawat, V. S., Mandal, M., & Kankanhalli, M. (2021) · arXiv · doi:10.48550/arxiv.2111.08947 · Cited by 190
Rethinking machine unlearning for large language models
Liu, S., Yao, Y., Jia, J., Casper, S., Baracaldo, N., Hase, P., Yao, Y., Liu, C. Y., Xu, X., Li, H., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00985-0 · Cited by 374
Metadata
- Authors
- Chen, C., Sun, F., Zhang, M., & Ding, B.
- Affiliations
- Tsinghua University (CN)
- Alibaba Group (China) (CN)
- Alibaba Group (United States) (US)
- Venue
- Proceedings of the ACM Web Conference 2022
- Publisher
- ACM
- Published
- 2022-04-25
- Pages
- 2768-2777
- OpenAlex
- W4224325687
- Cited by
- 110
- References
- 40
- Citations by year
- 2026: 22 · 2025: 42 · 2024: 25 · 2023: 19 · 2022: 2
- FWCI
- 12.13
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U21B2026)
- Tsinghua University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- recommender systems, machine unlearning, data deletion, privacy, collaborative filtering, sensitive data
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