This is the pytorch implementation of E2URec proposed in the paper Towards Efficient and Effective Unlearning of Large Language Models for Recommendation. (Frontiers of Computer Science 2024)

pip install -r requirements.txt

Scripts for data preprocessing are included in data_preprocess.

First, use ml-1m.ipynb to preprocess MovieLens-1M.

Then, convert data into text

python data2json.py --K 10 --temp_type simple --set train --dataset ml-1m
python data2json.py --K 10 --temp_type simple --set valid --dataset ml-1m
python data2json.py --K 10 --temp_type simple --set test --dataset ml-1m

Finally, use split_ml-1m.ipynb to split train/valid/test, retained/forgotten data.

Our method E2URec can be trained by

sh train_e2urec.sh

We also provide shell scripts for baselines.

To run the Retrain baseline:

sh train_normal.sh

To run the SISA baseline:

sh train_sisa.sh

To run the NegGrad baseline:

sh train_ga.sh

To run the Bad-T baseline:

sh train_rl.sh