Candidate selection using fuzzy logic, NLP, and deep learning techniques
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12139Keywords:
Fuzzy logic, Neural networks, Decision making, Machine learning, Artificial intelligence techniques, Natural language processingAbstract
The evaluation and selection of Curricula Vitae (CVs) is a complex process involving multiple factors. This study presents a model comprising two clearly distinct components. On the one hand, a multi-criteria evaluation and fuzzy selection system based on weights that are automatically calculated according to various characteristics. On the other hand, a natural language processing (NLP) system and a convolutional neural network (CNN), which take as input the output of the previous process and classify CVs according to five criteria. The use of this combined system helps to reduce subjectivity in recruitment decisions and improves the selection of the most suitable candidate for the position. The results obtained were compared against those from the Analytic Hierarchy Process (AHP) and the decisions made by a panel of human resources experts. A sample of over 1,000 CVs was used for this study. The study was conducted using a dataset of over 1000 CVs.
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