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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 unlearned model checkpointslogs/— Logs from training, retraining, and unlearning runs, including efficiency, utility, and effectiveness metricsdataset/— Instructions and scripts for downloading the datasets used in the benchmark
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