This is the PyTorch implementation of our paper published in ACM Transactions on Recommender Systems (ACM TORS):
Recommendation Unlearning via Influence Function
Yang Zhang, Zhiyu Hu, Yimeng Bai, Jiancan Wu, Qifan Wang, Fuli Feng.
Download the original datasets via the link provided in Data/download.txt, and preprocess them using the _data_process.py script.
All run scripts used in the paper are named according to the method, backbone, dataset, and other configurations.
For example, eraser_mf_amazon.py corresponds to the RecEraser method with MF as the backbone, applied on the Amazon dataset:
python eraser_mf_amazon.py
@article{IFRU,
author = {Zhang, Yang and Hu, Zhiyu and Bai, Yimeng and Wu, Jiancan and Wang, Qifan and Feng, Fuli},
title = {Recommendation Unlearning via Influence Function},
year = {2024},
issue_date = {June 2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {3},
number = {2},
url = {https://doi.org/10.1145/3701763},
doi = {10.1145/3701763},
journal = {ACM Trans. Recomm. Syst.},
month = dec,
articleno = {22},
numpages = {23},
keywords = {Recommender system, recommendation unlearning, privacy, influence function}
}