This is our implementation for the paper:
Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang. 2020. Neural Logic Reasoning. In ACM CIKM'20.
Please cite our paper if you use our codes. Thanks!
Author: Shaoyun Shi (shisy13 AT gmail.com)
@inproceedings{shi2020nlr,
title={Neural Logic Reasoning},
author={Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang},
booktitle={Proceedings of the 29th ACM International Conference on Information and Knowledge Management},
year={2020},
organization={ACM}
}
Python 3.7.3
Packages: See in requirements.txt
numpy==1.18.1
torch==1.0.1
pandas==0.24.2
scipy==1.3.0
tqdm==4.32.1
scikit_learn==0.23.1
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The processed datasets are in
./dataset/ -
ML-100k: The origin dataset can be found here.
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Amazon Datasets: The origin dataset can be found here.
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The codes for processing the data can be found in
./src/datasets/
- Some running commands can be found in
./command/command.py - For example:
# Neural Logic Reasong for recommendation on ML-100k dataset
> cd NLR/src/
> python main.py --rank 1 --model_name NLRRec --optimizer Adam --lr 0.001 --dataset ml100k01-1-5 --metric ndcg@10,precision@1 --max_his 10 --sparse_his 0 --neg_his 1 --l2 1e-4 --r_logic 1e-06 --r_length 1e-4 --random_seed 2018 --gpu 0