| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | tags: |
| | - recommender-system |
| | - machine-unlearning |
| | - benchmark |
| | pretty_name: ERASE |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | # 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](https://github.com/deem-data/erase-bench/). |
| |
|
| | ## Repository Contents |
| |
|
| | - **`saved/`** — Trained, retrained, and unlearned model checkpoints |
| | - **`logs/`** — Logs from training, retraining, and unlearning runs, including efficiency, utility, and effectiveness metrics |
| | - **`dataset/`** — Instructions and scripts for downloading the datasets used in the benchmark |
