Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem
Skills2Graph: Processing million Job Ads to face the Job Skill Mismatch Problem
Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Demo Track. Pages 4984-4987.
https://doi.org/10.24963/ijcai.2021/708
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.
Keywords:
Human-Computer Interaction: General
Natural Language Processing: General
Recommender Systems: General
Machine Learning: General