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.

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). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (pp.15885-15886). 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA : Association for the Advancement of Artificial Intelligence.

Skills2Job: A Recommender System that Encodes Job Offer Embeddings on Graph Databases (Student Abstract)

Seveso A.;Giabelli A.;Malandri L.;Mercorio F.;Mezzanzanica M.
2021

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.
No
paper
Scientifica
word embedding, AI, taxonomy learning, machine learning, graph-database;
English
35th AAAI Conference on Artificial Intelligence, AAAI 2021 - 2 February 2021 through 9 February 2021
9781713835974
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). In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (pp.15885-15886). 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA : Association for the Advancement of Artificial Intelligence.
Seveso, A; Giabelli, A; Malandri, L; Mercorio, F; Mezzanzanica, M
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/385830
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