deem-data/erase-bench
ERASE – A Real-World Aligned Benchmark for Unlearning in Recommender Systems ERASE is a benchmark for evaluating machine unlearning methods in recommender systems under real-world inspired conditions. It provides a standardized framework for measuring unlearning efficiency, utility, and effectiveness across multiple datasets and models. For full details, code, and documentation, see the GitHub repository. Repository Contents saved/ — Trained, retrained, and… See the full description on the dataset page: https://huggingface.co/datasets/deem-data/erase-bench.
| Author / organization | deem-data |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 538 (as reported by Hugging Face) |
| Likes | 3 |
| Created | 17 February 2026 |
| Last updated at source | 18 February 2026 |
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- 16 Oct 2026 Description: ERASE – A Real-World Aligned Benchmark for Unlearning in Recommender Systems ERASE is a benchmark for evaluating machine unlearning methods in recommender systems under real-world inspired conditions. It provides a standardized framework fo → ERASE – A Real-World Aligned Benchmark for Unlearning in Recommender Systems ERASE is a benchmark for evaluating machine unlearning methods in recommender systems under real-world inspired conditions. It provides a standardized framework fo
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