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.
“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]
Section 4.1 (page unavailable) says RecEraser unlearns data by retraining the affected submodel and aggregation component. The discussion of exact unlearning says its aim is complete removal of the requested data, but our copy does not say that this retraining mechanism naturally has a verifiability property. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMachine unlearning has played an essential role in many applications.
“Machine unlearning has played an essential role in many applications [33, 34].”
From Machine Unlearning: A Survey (2024) · cites this paper as [33]
The above unlearning schemes usually need to cache a large number of intermediate results to complete the unlearning process, which consumes a lot of storage space.
“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]
Section 4.1 describes retraining an affected submodel and the aggregation component; Section 5.3 discusses running-time efficiency. Neither addresses caching intermediate results or storage consumption. Searches for caching, checkpoints, and storage overhead found no passage supporting that claim. (LLM, conf=0.74)
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Unlearning Inversion Attacks for Graph Neural Networks
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Closed-form Machine Unlearning for Matrix Factorization
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Pre-training for Recommendation Unlearning
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QoSEraser: A Data Erasable Framework for Web Service QoS Prediction
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Forgetting User Preference in Recommendation Systems with Label-Flipping
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RUE: Realising Unlearning from the Perspective of Economics
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RecPS: Privacy Risk Scoring for Recommender Systems
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EFU: Enforcing Federated Unlearning via Functional Encryption
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Trojan Attack on Machine Unlearning: Security Risk of the Right to be Forgotten
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- 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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