Skills2Job: A Recommender System that Encodes Job Offer Embeddings on Graph Databases (Student Abstract)
Keywords:Recommender Systems, Graph Databases, Labor Market Intelligence, Word Embeddings
AbstractWe 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.
How to Cite
Seveso, A., Giabelli, A., Malandri, L., Mercorio, F., & Mezzanzanica, M. (2021). Skills2Job: A Recommender System that Encodes Job Offer Embeddings on Graph Databases (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15885-15886. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17939
AAAI Student Abstract and Poster Program