Skills2Graph: Processing Million Job Ads to Face the Job Skill Mismatch Problem
Abstract
In this paper, we present Skills2Graph, a tool that, starting from a set of users’ professional skills, identifies the most suitable jobs as they emerge from a large corpus of 2.5M+ Online Job Vacancies (OJVs) posted in three different countries (the United Kingdom, France, and Germany). To this aim, we rely both on co-occurrence statistics - computing a count-based measure of skill-relevance named Revealed Comparative Advantage (rca) - and distributional semantics - generating several embeddings on the OJVs corpus and performing an intrinsic evaluation of their quality. Results, evaluated through a user study of 10 labor market experts, show a high P@3 for the recommendations provided by Skills2Graph, and a high nDCG (0.985 and 0.984 in a [0,1] range), that indicates a strong correlation between the experts’ scores and the rankings generated by Skills2Graph.
Cite
Text
Giabelli et al. "Skills2Graph: Processing Million Job Ads to Face the Job Skill Mismatch Problem." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/708Markdown
[Giabelli et al. "Skills2Graph: Processing Million Job Ads to Face the Job Skill Mismatch Problem." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/giabelli2021ijcai-skills/) doi:10.24963/IJCAI.2021/708BibTeX
@inproceedings{giabelli2021ijcai-skills,
title = {{Skills2Graph: Processing Million Job Ads to Face the Job Skill Mismatch Problem}},
author = {Giabelli, Anna and Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario and Seveso, Andrea},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4984-4987},
doi = {10.24963/IJCAI.2021/708},
url = {https://mlanthology.org/ijcai/2021/giabelli2021ijcai-skills/}
}