https://github.com/justarter/E2URec
Official Code for paper "Towards Efficient and Effective Unlearning of Large Language Models for Recommendation" (Frontiers of Computer Science 2024)
https://github.com/justarter/E2URec
Last synced: about 2 months ago
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Official Code for paper "Towards Efficient and Effective Unlearning of Large Language Models for Recommendation" (Frontiers of Computer Science 2024)
- Host: GitHub
- URL: https://github.com/justarter/E2URec
- Owner: justarter
- Created: 2024-03-06T06:57:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-19T08:43:43.000Z (10 months ago)
- Last Synced: 2024-10-29T04:34:31.596Z (7 months ago)
- Language: Python
- Homepage:
- Size: 30.3 KB
- Stars: 34
- Watchers: 3
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-llm-unlearning - GitHub
README
# Towards Efficient and Effective Unlearning of Large Language Models for Recommendation
## Introduction
This is the pytorch implementation of ***E2URec*** proposed in the paper [Towards Efficient and Effective Unlearning of Large Language Models for Recommendation](http://arxiv.org/abs/2403.03536). (Frontiers of Computer Science 2024)
## Requirements
~~~python
pip install -r requirements.txt
~~~
## Data preprocess
Scripts for data preprocessing are included in data_preprocess.
First, use ml-1m.ipynb to preprocess MovieLens-1M.
Then, convert data into text
~~~python
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.
## How to run E2URec
Our method `E2URec` can be trained by
~~~python
sh train_e2urec.sh
~~~
## How to run baselines
We also provide shell scripts for baselines.
To run the `Retrain` baseline:
~~~python
sh train_normal.sh
~~~
To run the `SISA` baseline:
~~~python
sh train_sisa.sh
~~~
To run the `NegGrad` baseline:
~~~python
sh train_ga.sh
~~~
To run the `Bad-T` baseline:
~~~python
sh train_rl.sh
~~~