Datasets:

deem-data
/

erase-bench

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.

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