Recommendation networks in human resource selection
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.34627/rcc.v13i0.149 |
Resumo: | One of the main challenges in a human resources recruitment process is the difficulty of analyzing objectively all candidates. This paper shows the application of networks of professional recommendations among peers in human resources recruitment processes. For this purpose we use the LinkedIn social network and in particular the recommendations made by a subset of professionals. We generate a network of recommendations by specialty, where the vertices are the professionals and the arcs are the recommendation relationships. Based on the network of recommendations we apply the algorithm PageRank to order of each professional by specialty. The analysis for several specialties is performed using multi-criteria evaluation, where the TOPSIS method is applied. |
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7160 |
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Recommendation networks in human resource selectionRedes de Recomendações na Seleção de Recursos HumanosOne of the main challenges in a human resources recruitment process is the difficulty of analyzing objectively all candidates. This paper shows the application of networks of professional recommendations among peers in human resources recruitment processes. For this purpose we use the LinkedIn social network and in particular the recommendations made by a subset of professionals. We generate a network of recommendations by specialty, where the vertices are the professionals and the arcs are the recommendation relationships. Based on the network of recommendations we apply the algorithm PageRank to order of each professional by specialty. The analysis for several specialties is performed using multi-criteria evaluation, where the TOPSIS method is applied.Um dos principais desafios encontrados num processo de recrutamento de recursos humanos prende-se com a dificuldade prática de analisar de forma objetiva todos os candidatos. Neste trabalho mostra-se a aplicabilidade das redes de recomendações profissionais entre pares no âmbito de processos de recrutamento de recursos humanos. Para o efeito utilizamos a rede social LinkedIn e em particular as recomendações efetuadas por um conjunto de profissionais. Geramos uma rede de recomendações por especialidade, em que os vértices são os profissionais analisados e os arcos são as relações de recomendação. Sobre a rede de recomendações aplica-se o algoritmo PageRank para ordenar cada profissional por especialidade. A análise para várias especialidades é realizada através da avaliação multicritério, onde é aplicado o método TOPSIS.Universidade Aberta2018-12-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.34627/rcc.v13i0.149oai:ojs2.journals.uab.pt:article/149Revista de Ciências da Computação; v. 13 (2018); 25-362182-18011646-633010.34627/rcc.v13i0reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPporhttps://journals.uab.pt/index.php/rcc/article/view/149https://doi.org/10.34627/rcc.v13i0.149https://journals.uab.pt/index.php/rcc/article/view/149/108Direitos de Autor (c) 2018 Universidade Abertahttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessFernandes, DavidCavique, Luís2022-10-25T11:31:56Zoai:ojs2.journals.uab.pt:article/149Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:14:01.456229Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Recommendation networks in human resource selection Redes de Recomendações na Seleção de Recursos Humanos |
title |
Recommendation networks in human resource selection |
spellingShingle |
Recommendation networks in human resource selection Fernandes, David |
title_short |
Recommendation networks in human resource selection |
title_full |
Recommendation networks in human resource selection |
title_fullStr |
Recommendation networks in human resource selection |
title_full_unstemmed |
Recommendation networks in human resource selection |
title_sort |
Recommendation networks in human resource selection |
author |
Fernandes, David |
author_facet |
Fernandes, David Cavique, Luís |
author_role |
author |
author2 |
Cavique, Luís |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Fernandes, David Cavique, Luís |
description |
One of the main challenges in a human resources recruitment process is the difficulty of analyzing objectively all candidates. This paper shows the application of networks of professional recommendations among peers in human resources recruitment processes. For this purpose we use the LinkedIn social network and in particular the recommendations made by a subset of professionals. We generate a network of recommendations by specialty, where the vertices are the professionals and the arcs are the recommendation relationships. Based on the network of recommendations we apply the algorithm PageRank to order of each professional by specialty. The analysis for several specialties is performed using multi-criteria evaluation, where the TOPSIS method is applied. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-10 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.34627/rcc.v13i0.149 oai:ojs2.journals.uab.pt:article/149 |
url |
https://doi.org/10.34627/rcc.v13i0.149 |
identifier_str_mv |
oai:ojs2.journals.uab.pt:article/149 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://journals.uab.pt/index.php/rcc/article/view/149 https://doi.org/10.34627/rcc.v13i0.149 https://journals.uab.pt/index.php/rcc/article/view/149/108 |
dc.rights.driver.fl_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos de Autor (c) 2018 Universidade Aberta http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Aberta |
publisher.none.fl_str_mv |
Universidade Aberta |
dc.source.none.fl_str_mv |
Revista de Ciências da Computação; v. 13 (2018); 25-36 2182-1801 1646-6330 10.34627/rcc.v13i0 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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