Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem
Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso
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.
BibTeX
@inproceedings{ijcai2021p708,
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 = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4984--4987},
year = {2021},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2021/708},
url = {https://doi.org/10.24963/ijcai.2021/708},
}