A novel approach for job matching and skill recommendation using transformers and the O*NET database

Today we have tons of information posted on the web every day regarding job supply and demand which has heavily affected the job market. The online enrolling process has thus become efficient for applicants as it allows them to present their resumes using the Internet and, as such, simultaneously to...

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Detalhes bibliográficos
Autores: Alonso, Rubén, Dessí, Danilo, Meloni, Antonello, Reforgiato Recupero, Diego
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/419159
Acesso em linha:http://hdl.handle.net/10261/419159
Access Level:Acceso aberto
Palavra-chave:Information extraction
Transformers
Online enrolling process
Natural language processing
Course recommendation
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spelling A novel approach for job matching and skill recommendation using transformers and the O*NET databaseAlonso, RubénDessí, DaniloMeloni, AntonelloReforgiato Recupero, DiegoInformation extractionTransformersOnline enrolling processNatural language processingCourse recommendationToday we have tons of information posted on the web every day regarding job supply and demand which has heavily affected the job market. The online enrolling process has thus become efficient for applicants as it allows them to present their resumes using the Internet and, as such, simultaneously to numerous organizations. Online systems such as Monster.com, OfferZen, and LinkedIn contain millions of job offers and resumes of potential candidates leaving to companies with the hard task to face an enormous amount of data to manage to select the most suitable applicant. The task of assessing the resumes of candidates and providing automatic recommendations on which one suits a particular position best has, therefore, become essential to speed up the hiring process. Similarly, it is important to help applicants to quickly find a job appropriate to their skills and provide recommendations about what they need to master to become eligible for certain jobs. Our approach lies in this context and proposes a new method to identify skills from candidates’ resumes and match resumes with job descriptions. We employed the O*NET database entities related to different skills and abilities required by different jobs; moreover, we leveraged deep learning technologies to compute the semantic similarity between O*NET entities and part of text extracted from candidates’ resumes. The ultimate goal is to identify the most suitable job for a certain resume according to the information there contained. We have defined two scenarios: i) given aresume, identify the top O*NET occupations with the highest match with the resume, ii) given a candidate’s resume and a set of job descriptions, identify which one of the input jobs is the most suitable for the candidate. The evaluation that has been carried out indicates that the proposed approach outperforms the baselines in the two scenarios. Finally, we provide a use case for candidates where it is possible to recommend courses with the goal to fill certain skills and make them qualified for a certain job.This research was partially funded by the Eupean Un project STAR -Novel AI technology for dynamic and unpredictable manufacturing environments (grant number 956573).Peer reviewedElsevierEuropean CommissionReforgiato Recupero, Diego [0000-0001-8646-6183]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/419159reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.bdr.2025.100509Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4191592026-05-22T06:33:51Z
dc.title.none.fl_str_mv A novel approach for job matching and skill recommendation using transformers and the O*NET database
title A novel approach for job matching and skill recommendation using transformers and the O*NET database
spellingShingle A novel approach for job matching and skill recommendation using transformers and the O*NET database
Alonso, Rubén
Information extraction
Transformers
Online enrolling process
Natural language processing
Course recommendation
title_short A novel approach for job matching and skill recommendation using transformers and the O*NET database
title_full A novel approach for job matching and skill recommendation using transformers and the O*NET database
title_fullStr A novel approach for job matching and skill recommendation using transformers and the O*NET database
title_full_unstemmed A novel approach for job matching and skill recommendation using transformers and the O*NET database
title_sort A novel approach for job matching and skill recommendation using transformers and the O*NET database
dc.creator.none.fl_str_mv Alonso, Rubén
Dessí, Danilo
Meloni, Antonello
Reforgiato Recupero, Diego
author Alonso, Rubén
author_facet Alonso, Rubén
Dessí, Danilo
Meloni, Antonello
Reforgiato Recupero, Diego
author_role author
author2 Dessí, Danilo
Meloni, Antonello
Reforgiato Recupero, Diego
author2_role author
author
author
dc.contributor.none.fl_str_mv European Commission
Reforgiato Recupero, Diego [0000-0001-8646-6183]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Information extraction
Transformers
Online enrolling process
Natural language processing
Course recommendation
topic Information extraction
Transformers
Online enrolling process
Natural language processing
Course recommendation
description Today we have tons of information posted on the web every day regarding job supply and demand which has heavily affected the job market. The online enrolling process has thus become efficient for applicants as it allows them to present their resumes using the Internet and, as such, simultaneously to numerous organizations. Online systems such as Monster.com, OfferZen, and LinkedIn contain millions of job offers and resumes of potential candidates leaving to companies with the hard task to face an enormous amount of data to manage to select the most suitable applicant. The task of assessing the resumes of candidates and providing automatic recommendations on which one suits a particular position best has, therefore, become essential to speed up the hiring process. Similarly, it is important to help applicants to quickly find a job appropriate to their skills and provide recommendations about what they need to master to become eligible for certain jobs. Our approach lies in this context and proposes a new method to identify skills from candidates’ resumes and match resumes with job descriptions. We employed the O*NET database entities related to different skills and abilities required by different jobs; moreover, we leveraged deep learning technologies to compute the semantic similarity between O*NET entities and part of text extracted from candidates’ resumes. The ultimate goal is to identify the most suitable job for a certain resume according to the information there contained. We have defined two scenarios: i) given aresume, identify the top O*NET occupations with the highest match with the resume, ii) given a candidate’s resume and a set of job descriptions, identify which one of the input jobs is the most suitable for the candidate. The evaluation that has been carried out indicates that the proposed approach outperforms the baselines in the two scenarios. Finally, we provide a use case for candidates where it is possible to recommend courses with the goal to fill certain skills and make them qualified for a certain job.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/419159
url http://hdl.handle.net/10261/419159
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.bdr.2025.100509
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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