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.

Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M., Seveso, A. (2021). Skills2Graph: Processing Million Job Ads to face the Job Skill Mismatch Problem. In 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp.4984-4987). International Joint Conferences on Artificial Intelligence [10.24963/ijcai.2021/708].

Skills2Graph: Processing Million Job Ads to face the Job Skill Mismatch Problem

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

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.
No
paper
Scientifica
Lmi; machine learning; skill intelligence, word embedding, ai, graph-database
English
30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - 19 August 2021 through 27 August 2021
978-0-9992411-9-6
Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M., Seveso, A. (2021). Skills2Graph: Processing Million Job Ads to face the Job Skill Mismatch Problem. In 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp.4984-4987). International Joint Conferences on Artificial Intelligence [10.24963/ijcai.2021/708].
Giabelli, A; Malandri, L; Mercorio, F; Mezzanzanica, M; Seveso, A
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324408
Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
Social impact