This is our implementation of the paper:
Chong Chen, Fei Sun, Min Zhang and Bolin Ding. 2022. Recommendation Unlearning. In TheWebConf'22.
Please cite our TheWebConf'22 paper if you use our codes. Thanks!
@inproceedings{chen2022recommendation,
title={Recommendation Unlearning},
author={Chen, Chong and Sun, Fei and Zhang, Min and Ding, Bolin},
booktitle={Proceedings of The Web Conference},
year={2022},
}
Author: Chong Chen ([email protected])
We use C++ code to output metrics during and after training, as used in LightGCN, which is much more efficient than python evaluator. It needs to be compiled first using the following command:
python setup.py build_ext --inplace
After compilation, the C++ code will run by default instead of Python code.
The code of data partition is in code/utility/data_partition.py.
The pre-train embedding vectors are computed by WMF in this work.
The instruction of commands has been stated in the codes (see the parser function in code/utility/parser.py).
The hype-parameters for base models are:
yelp2018:
BPR: adagrade lr=0.05 reg=0.01 batch=256
WMF: adagrade lr=0.05 reg=0.01 batch=256 weight=0.05 drop=0.7
LightGCN: adam lr=0.001 reg=1e-4 batch=1024
ml-1m:
BPR: adagrade lr=0.05 reg=0.01 batch=256
WMF: adagrade lr=0.05 reg=0.01 batch=256 weight=0.2 drop=0.7
LightGCN: adam lr=0.001 reg=1e-3 batch=1024
ml-10m:
BPR: adagrade lr=0.05 reg=0.001 batch=256
WMF: adagrade lr=0.05 reg=0.01 batch=256 weight=0.2 drop=0.7
LightGCN: adam lr=0.001 reg=1e-3 batch=1024
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