Publicação
Advanced selection system for automated human resources management
| Resumo: | This dissertation’s aim is to develop an innovative automated sourcing system that leverages advanced technologies to predict the suitability of a human resource based on their experience. In this case, sourcing refers to the process of identifying, attracting, and acquiring potential candidates for a job position. The ultimate goal is to present a generic and highly accurate model capable of analysing a diverse set of candidates automatically. This sourcing of human resources traditionally takes up a large amount of time and effort. Furthermore, accurately predicting the capabilities that individuals can bring to a team represents a multifaceted challenge. The complexity lies in the need to integrate various factors, including experience, knowledge, and a myriad of skills. As such, this research aims to bridge this gap by creating a sophisticated system that combines these elements effectively. The performance of the HR department as a whole can be greatly improved by establishing an automated system. The ability to predict a candidate’s talents with accuracy and simplify the sourcing process guarantees that the right person is found decisively, which promotes more effective team building. Organisations can manage resources and enhance the strategic decision-making processes in the human resources area by automating these complex operations. The development of this system involves a comprehensive process that begins with the extraction of crucial information from candidate CVs (Curriculum Vitae). Obtaining relevant details on the backgrounds and education of candidates depends heavily on this first phase. After the data has been extracted, it is analysed using a systematic approach to identify and emphasise the key elements required to estimate an applicant’s skills and compatibility with a job description. Through the integration of advanced processing and key field identification with information extraction from CVs, the developed system seeks to provide an automated solution to the challenge outlined in the thesis. |
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| Autores principais: | Correia, Mário Jorge Amaral Pinto |
| Assunto: | Machine Learning Predictive modeling CV Parsing Recruitment automation Automated HR Management Information extraction Natural Language Processing (NLP) Python Aprendizagem automática Modelação preditiva Análise de CVs Automatização do recrutamento Gestão automatizada de RH Extração de informação Processamento de Linguagem Natural (PNL) Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | This dissertation’s aim is to develop an innovative automated sourcing system that leverages advanced technologies to predict the suitability of a human resource based on their experience. In this case, sourcing refers to the process of identifying, attracting, and acquiring potential candidates for a job position. The ultimate goal is to present a generic and highly accurate model capable of analysing a diverse set of candidates automatically. This sourcing of human resources traditionally takes up a large amount of time and effort. Furthermore, accurately predicting the capabilities that individuals can bring to a team represents a multifaceted challenge. The complexity lies in the need to integrate various factors, including experience, knowledge, and a myriad of skills. As such, this research aims to bridge this gap by creating a sophisticated system that combines these elements effectively. The performance of the HR department as a whole can be greatly improved by establishing an automated system. The ability to predict a candidate’s talents with accuracy and simplify the sourcing process guarantees that the right person is found decisively, which promotes more effective team building. Organisations can manage resources and enhance the strategic decision-making processes in the human resources area by automating these complex operations. The development of this system involves a comprehensive process that begins with the extraction of crucial information from candidate CVs (Curriculum Vitae). Obtaining relevant details on the backgrounds and education of candidates depends heavily on this first phase. After the data has been extracted, it is analysed using a systematic approach to identify and emphasise the key elements required to estimate an applicant’s skills and compatibility with a job description. Through the integration of advanced processing and key field identification with information extraction from CVs, the developed system seeks to provide an automated solution to the challenge outlined in the thesis. |
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