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Reviewer-Rec

Task

Here are several examples of reviewer matching by using TD-IDF, LightGCN and GF-CF. Please refer to the following steps to run our codes.

Requirements

  • Python version >= 3.6
  • PyTorch version >= 1.6.0
  • Network for initial run
  • pip install -r requirements.txt (Note: sparsesvd can be installed from source)

Processed Data

We provide the processed data in data-4k and data-8k. We also provide the reviewer profiles linked to Open Academic Graph (OAG), where each reviewer is associated with respective published papers [Matchings Part-1 Download] [Matchings Part-2 Download].

Usage

TF-IDF

We use the embedding method of TF-IDF, the pretrained vectorizer and model is provided via Aliyun, and put this folder as reviewer_rec_TFIDF/get_paper_embedding/embedding_data. Also, download the data from Here, and put this folder as reviewer_rec_TFIDF/get_paper_embedding/data.

Moreover, download tfidf_model.pkl from Baidu Pan with password jey2 or Aliyun and svd_model.pkl from Baidu Pan with password qcng or Aliyun. Put these two models in into reviewer_rec_TFIDF/get_paper_embedding/.

Download paper_embedding.json from https://pan.baidu.com/s/1mvNnpRY6fWOM4mUE3WsZQQ?pwd=7suq or Aliyun and training_reviewer_embedding.json from https://pan.baidu.com/s/1ish6ofqTm5dPiz0PpDQ9Hg?pwd=8jy8 or Aliyun. Put these two embedding files into reviewer_rec_TFIDF/get_paper_embedding/embedding_data.

Please run:

cd reviewer_rec_TFIDF
python parse_paper_information.py

to get the results.

GF-CF and LightGCN

This code is heavliy bulit on the official implementation of GF-CF and LightGCN.

Download the data from Here, and put this folder as /reviewer_rec_LightGCN/data/reviewer_rec.

To run LightGCN, use the following command:

cd reviewer_rec_LightGCN
python main.py --dataset="reviewer_rec" --topks="[20,5,10,50]" --model "lgn" --gpu_id 0

To run GF-CF, use the following command:

cd reviewer_rec_LightGCN
python main.py --dataset="reviewer_rec" --topks="[20,5,10,50]" --simple_model "gf-cf" --gpu_id 0

References

🌟 If you find our work helpful, please leave us a star and cite our paper.

@inproceedings{zhang2024oag,
  title={OAG-bench: a human-curated benchmark for academic graph mining},
  author={Fanjin Zhang and Shijie Shi and Yifan Zhu and Bo Chen and Yukuo Cen and Jifan Yu and Yelin Chen and Lulu Wang and Qingfei Zhao and Yuqing Cheng and Tianyi Han and Yuwei An and Dan Zhang and Weng Lam Tam and Kun Cao and Yunhe Pang and Xinyu Guan and Huihui Yuan and Jian Song and Xiaoyan Li and Yuxiao Dong and Jie Tang},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={6214--6225},
  year={2024}
}