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This is a Experimental Study study.

Smart Application for Recruiting Based on Natural Language Processing Methods, Transformer Models and Siamese Neural Network Architecture

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This approach demonstrates semantic similarity for resume-vacancy matching in HR applications, highlighting the risk of algorithmic bias.

The proposed model achieved 72% accuracy in matching resumes and job vacancies, significantly outperforming traditional methods.
Extensive experiments on a dataset of 7,000 resume-vacancy pairs confirm the model's robustness in identifying semantic compatibility.
Using siamese neural networks, the system learns complex relationships between resumes and vacancies to provide accurate matches.
The study emphasizes the importance of addressing algorithmic bias through ongoing equity audits and human validation.

Vyshotravka et al. (2025) studied this question.

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