Neural Logic Reasoning

This is our implementation for the paper:

Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang. 2020. Neural Logic Reasoning In 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}
}

Environments

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

Datasets

The processed datasets are in ./dataset/
ML-100k: The origin dataset can be found here.
Amazon Datasets: The origin dataset can be found here.
The codes for processing the data can be found in ./src/datasets/

Example to run the codes

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

evison/NLR

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