Human Resources Recommender system based on discrete variables
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/129696 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
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7160 |
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Human Resources Recommender system based on discrete variablesText MiningResume parserWord EmbeddingsRecommender systemStarSpaceDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing and Understanding has become one of the most exciting and challenging fields in the area of Artificial Intelligence and Machine Learning. With the rapidly changing business environment and surroundings, the importance of having the data transformed in such a way that makes it easy to interpret is the greatest competitive advantage a company can have. Having said this, the purpose of this thesis dissertation is to implement a recommender system for the Human Resources department in a company that will aid the decision-making process of filling a specific job position with the right candidate. The recommender system fill be fed with applicants, each being represented by their skills, and will produce a subset of most adequate candidates given a job position. This work uses StarSpace, a novelty neural embedding model, whose aim is to represent entities in a common vectorial space and further perform similarity measures amongst them.Henriques, Roberto André PereiraRUNSarovska, Dina2021-12-23T17:56:44Z2021-12-022021-12-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/129696TID:202834352enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:08:50Zoai:run.unl.pt:10362/129696Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:40.176347Repositório 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 |
Human Resources Recommender system based on discrete variables |
title |
Human Resources Recommender system based on discrete variables |
spellingShingle |
Human Resources Recommender system based on discrete variables Sarovska, Dina Text Mining Resume parser Word Embeddings Recommender system StarSpace |
title_short |
Human Resources Recommender system based on discrete variables |
title_full |
Human Resources Recommender system based on discrete variables |
title_fullStr |
Human Resources Recommender system based on discrete variables |
title_full_unstemmed |
Human Resources Recommender system based on discrete variables |
title_sort |
Human Resources Recommender system based on discrete variables |
author |
Sarovska, Dina |
author_facet |
Sarovska, Dina |
author_role |
author |
dc.contributor.none.fl_str_mv |
Henriques, Roberto André Pereira RUN |
dc.contributor.author.fl_str_mv |
Sarovska, Dina |
dc.subject.por.fl_str_mv |
Text Mining Resume parser Word Embeddings Recommender system StarSpace |
topic |
Text Mining Resume parser Word Embeddings Recommender system StarSpace |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-23T17:56:44Z 2021-12-02 2021-12-02T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/129696 TID:202834352 |
url |
http://hdl.handle.net/10362/129696 |
identifier_str_mv |
TID:202834352 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138070052732928 |