This is an official repository of the paper Skill-based Career Path Modeling and Recommendation published in IEEE Big Data 2020 Conference.
Create a python enviroment with the provided requirements file and miniconda:
conda create --name mnss --file requirements.txt
conda activate mnss
MNSS model is added in mnss.py and NSS and NEMO model are added in baseline.py. The training command is:
python train.py --mode <model_name: mnss, nss, or nemo>
The monotonic GRU module is defined in monotonic_gru.py.
Linkedin dataset is retreived from Kaggle and Indeed dataset is collected from Datastock. Linkedin dataset is not available anymore in Kaggle. Unfortunately, due to agreement with Kaggle, we cannot release the collected Linkedin dataset.
We added a sample dataset demo.json and required mappers in the data/demo.zip file for running train.py (unzip it).
If you find this code useful in your research then please cite
@inproceedings{ghosh2020skill,
title={Skill-based Career Path Modeling and Recommendation},
author={Ghosh, Aritra and Woolf, Beverly and Zilberstein, Shlomo and Lan, Andrew},
booktitle={2020 IEEE International Conference on Big Data (Big Data)},
pages={1156--1165},
year={2020},
organization={IEEE}
}
Contact: Aritra Ghosh (aritraghosh.iem@gmail.com).