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Human resources sptimization with multilayer perceptron: an automated selection tool

2025Working paper Open access
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorNeto, Reginaldo G. de S.
dc.contributor.authorJatobá, Mariana N.
dc.contributor.authorSantana, Matheus
dc.contributor.authorFernandes, Paula Sdete
dc.contributor.authorFerreira, João J.
dc.contributor.authorFoleis, Juliano Henrique
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2025-07-11T09:14:33Z
dc.date.available2025-07-11T09:14:33Z
dc.date.issued2025
dc.description.abstractThis study aims to create an artificial neural network (ANN) model with a multi-layer perceptron (MLP) architecture, designed to analyze the CVs of candidates for the position of sales consultant. To do this, a database of 600 CVs cataloged with scores from 0 to 10 by specialists with experience in recruitment and selection (R&S) is used. Fourteen characteristics are extracted from each CV, including ordinal and nominal attributes. A model with 3 hidden layers is used, which is trained with a split of 80% for training and 20% for testing. The activation function chosen for the hidden layers is the Rectified Linear Unit (ReLU), using the "adam" optimizer with a backpropagation algorithm during training using the Mean Squared Error (MSE) performance metric. The results show that the model is effective, giving a Mean Absolute Error (MAE) of 0.33, MSE of 0.37, Root Mean Squared Error (RMSE) of 0.61, and an r² Score of 0.96. These data not only confirm MLP's ability to replicate human accuracy but also suggest that such technologies can provide a faster and less biased tool for evaluating CVs. This performance of ANN indicates avenues for future research into the integration of other Artificial Intelligence (AI) technologies to refine the interpretation of less quantifiable characteristics, as well as making the process of R&S of new candidates in companies more agile.eng
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DSI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DSI: 10.54499/UIDB/05757/2020); SusTEC, LA/P/0007/2020 (DSI: 10.54499/LA/P/0007/2020); UNIAG, UIDB/04752/2020 (DSI 10.54499/UIDB/04752/2020) and UIDP/04752/2020 (DSI 10.54499/UIDP/04752/2020); and NECE, UIDB/04630/2020 (DSI 0.54499/UIDP/04630/2020). The authors are also grateful to National funding by FCT, Foundation for Science and Technology, through the individual research grant BD/10678/2022 of Mariana Namen Jatobá.
dc.identifier.citationNeto, Reginaldo G. de S.; Jatobá, Mariana N.; Santana, Matheus; Fernandes, Paula Sdete; Ferreira, João J.; Foleis, Juliano Henrique; Teixeira, João Paulo. (2025). Human resources sptimization with multilayer perceptron: an automated selection tool. Procedia Computer Science. ISSN 1877-0509. 256, p. 238-245
dc.identifier.doi10.1016/j.procs.2025.02.117
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/10198/34662
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationApplied Management Research Unit
dc.relationMountain Research Center
dc.relationResearch Unit in Business Sciences
dc.relation.ispartofProcedia Computer Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial Intelligence
dc.subjectRecruitment
dc.subjectCV
dc.subjectClassification
dc.subjectMLP
dc.subjectHR Management
dc.titleHuman resources sptimization with multilayer perceptron: an automated selection tooleng
dc.typeworking paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleApplied Management Research Unit
oaire.awardTitleMountain Research Center
oaire.awardTitleResearch Unit in Business Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04752%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04630%2F2020/PT
oaire.citation.endPage245
oaire.citation.startPage238
oaire.citation.titleProcedia Computer Science
oaire.citation.volume256
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameTeixeira
person.givenNameJoão Paulo
person.identifier663194
person.identifier.ciencia-id4F15-B322-59B4
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