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This is a Quantitative Study study.

Automatic Resume Screening Using SBERT

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The study develops an automated screening system using machine learning and natural language processing, suggesting it improves recruitment fairness.

The system automates resume screening, dramatically improving recruitment efficiency and reducing bottlenecks.
Utilizing a pre-trained SBERT model, the approach captures semantic similarities and keyword overlaps for effective comparison.
A Random Forest Classifier predicts a probabilistic suitability score, enabling accurate candidate ranking based on qualifications.
The dual-portal web application provides personalized feedback for applicants, improving their fit for roles while assisting recruiters.

Rashi et al. (2025) studied this question.

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The study develops an automated screening system using machine learning and natural language processing, suggesting it improves recruitment fairness.