Abstract: Now-a-days, the use of web portals known as job boards for publishing job offers by recruiters has grown consid-erably. The candidates in their turn, apply to the job positions via the job boards. Since the opportunities are available on a wide range and the job application process is fast and straightforward, the data flow is transformed to large-volume data sets which are hard to handle. Most companies tend to automate the candidate selection process that aims to match the job offers with suitable resumes. In this paper, we propose a supervised learning approach to classify the job offers and CVs shared in the recruitment sites in order to enhance automatic recruitment process. We used natural language processing techniques for job offers and CV preprocessing. Next, we used word embeddings and deep neural networks to train two models, the first one categorizes recruitment documents based on job skills, and the second one predicts the expertise degree class. The experiment results show that our proposal is very efficient.

Amine Habous and El Habib Nfaoui, “Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain” International Journal of Advanced Computer Science and Applications(IJACSA), 12(7), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120774

@article{Habous2021,
title = {Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120774},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120774},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {7},
author = {Amine Habous and El Habib Nfaoui}
}