Neural Logic Reasoning

This repository includes the implementation for Neural Logic Reasoning (NLR):

Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang. 2020. Neural Logic Reasoning. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20).

Refernece

For inquiries contact Shaoyun Shi (shisy13@gmail.com) or Yongfeng Zhang (yongfeng.zhang@rutgers.edu)

@inproceedings{shi2020neural,
  title={Neural Logic Reasoning},
  author={Shi, Shaoyun and Chen, Hanxiong and Ma, Weizhi and Mao, Jiaxin and Zhang, Min and Zhang, Yongfeng},
  booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
  pages={1365--1374},
  year={2020}
}
@inproceedings{chen2021neural,
  title={Neural Collaborative Reasoning},
  author={Chen, Hanxiong and Shi, Shaoyun and Li, Yunqi and Zhang, Yongfeng},
  booktitle={Proceedings of the Web Conference 2021},
  pages={1516--1527},
  year={2021}
}

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/

• Logic-1k: Dataset for solving logical equations with 1k variables

• Logic-10k: Dataset for solving logical equations with 10k variables

• 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