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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].”
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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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Zhang, Y., Li, S., Lin, M., Shen, J., Yu, S., & Lin, J. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033239
Machine Unlearning In Recommendation Systems
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Prompt-Tuning for Recommendation Unlearning
Huang, J., Fan, Z., Morishetti, L., Guo, Y., Nag, K., Ahn, H., Chen, Z., & Tolomei, G. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00152
Recommendation Unlearning with Dynamic Sampling and Interest Boundary Perception
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Graph Unlearning System with Subgraph De-Isolation Measures
Li, Y., Cheng, D., Zhang, G., Li, C., & Zhang, S. (2025) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3750734
Selective Forgetting in Document Images Using Enhanced Ensembles
Mashhood, M., Moetesum, M., Faisal Shafait, B., & Ul-Hasan, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04617-8_5
FELEMN: Toward Efficient Feature-Level Machine Unlearning for Exact Privacy Protection
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Machine Unlearning Scheme for Recommendation System Based on Gradient Attribution
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NPFGLU: Unlearning Links in Graph Neural Networks
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The Evaluation of Retrieval-Based Unlearning Mechanisms on Large Language Models
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Improving Unlearning with Model Updates Probably Aligned with Gradients
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RecPS: Privacy Risk Scoring for Recommender Systems
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Fairness-aware Graph Unlearning with Knowledge Distillation
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Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning
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Privacy Matters: Data Attack to Make User Preferences Unlearnable in Recommendation
Shao, P., Wu, L., Zhang, K., Chen, L., & Wang, M. (2026) · ACM Transactions on Information Systems · doi:10.1145/3803545
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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