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.
- 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)
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].
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.
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