Skills2Job: A Recommender System That Encodes Job Offer Embeddings on Graph Databases (Student Abstract)
Abstract
We propose a recommender system that, starting from a set of users skills, identifies the most suitable jobs as they emerge from a large text of Online Job Vacancies (OJVs). To this aim, we process 2.5M+ OJVs posted in three different countries (United Kingdom, France and Germany), generating several embeddings and performing an intrinsic evaluation of their quality. Besides, we compute a measure of skill importance for each occupation in each country, the Revealed Comparative Advantage (rca). The best vector models, together with the rca, are used to feed a graph database, which will serve as the keystone for the recommender system. Finally, a user study of 10 validates the effectiveness of Skills2Job, both in terms of precision and nDGC.
Cite
Text
Seveso et al. "Skills2Job: A Recommender System That Encodes Job Offer Embeddings on Graph Databases (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17939Markdown
[Seveso et al. "Skills2Job: A Recommender System That Encodes Job Offer Embeddings on Graph Databases (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/seveso2021aaai-skills/) doi:10.1609/AAAI.V35I18.17939BibTeX
@inproceedings{seveso2021aaai-skills,
title = {{Skills2Job: A Recommender System That Encodes Job Offer Embeddings on Graph Databases (Student Abstract)}},
author = {Seveso, Andrea and Giabelli, Anna and Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {15885-15886},
doi = {10.1609/AAAI.V35I18.17939},
url = {https://mlanthology.org/aaai/2021/seveso2021aaai-skills/}
}