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dc.rights.licensehttps://creativecommons.org/licenses/by-sa/2.5/ar/es_AR
dc.contributor.advisorDel Corro, Luciano
dc.contributor.authorRivas, Richardes_AR
dc.date.accessioned2024-07-26T22:01:36Z
dc.date.available2024-07-26T22:01:36Z
dc.date.issued2024
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/12917
dc.description.abstractThe present work aims to create a Machine Learning model using unstructured text data in order to predict whether a position is prone to taking a longer Time to Fill than the overall average. As well as building an initial categorization of profiles within the organizations in which it was carried out, this will help to provide insights that allow understanding both the demand and supply of different job profiles using different Natural Language Processing and Unsupervised Machine Learning techniques. The processing of the text data will be done by using an open source Language Model (LLM) in order to generate their corresponding document embeddings.es_AR
dc.format.extent73 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.publisherUniversidad Torcuato Di Tellaes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectPredicción tecnológicaes_AR
dc.subjectTechnological Predictiones_AR
dc.subjectNatural Language Processinges_AR
dc.titleJob profile demand understanding in international financial organizations: a natural language processing approaches_AR
dc.typeinfo:eu-repo/semantics/masterThesises_AR
dc.typeinfo:ar-repo/semantics/tesis de maestríaes
thesis.degree.nameMaster in Management + Analyticsen
dc.subject.keywordAprendizaje automáticoes_AR
dc.subject.keywordMachine Learninges_AR
dc.subject.keywordProcesamiento de Lenguaje Naturales_AR
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_AR


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