Official PyTorch implementation for Search History enhanced Person-Job Fit model.
Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Shuqing Bian, Yang Song, Tao Zhang, Ji-Rong Wen. Leveraging Search History for Improving Person-Job Fit. DASFAA 2022.
python==3.7.7
pytorch==1.7.1
PyYAML==6.0
numpy==1.21.5
scikit_learn==1.0.2
python preprocess.py
# After around 20 minutes' preprocessing
python main.pyOur dataset will be released when it's applicable.
After downloading and unzipping, these files should be moved to dataset/:
dataset/
├── data.{train/valid/test} # <geek_id, job_id> pairs
├── data.{train/valid/test}.bert.npy # BERT representation for <resume, job desc> pairs
├── data.search.{train/valid/test} # search history
├── {geek/job}.token # geeks (or jobs) IDs
├── geek.low # whether a geek is labeled as a low-skilled candidate
├── job.search.token # job IDs exist only in search history
├── {geek/job}.longsent # resumes or job descriptions
├── word.cnt # word frequency
└── word.search.id # words exist only in search history
geekdenotes candidates or job seekers.
The main framework is reference to RecBole.
If you use this code for your research, please cite the following paper.
@inproceedings{hou2022shpjf,
author = {Yupeng Hou and Xingyu Pan and Wayne Xin Zhao and Shuqing Bian and Yang Song and Tao Zhang and Ji-Rong Wen},
title = {Leveraging Search History for Improving Person-Job Fit},
booktitle = {{DASFAA}},
year = {2022}
}