Recruitment processes in the modern era are swamped with a high volume of applications, making manual resume screening a significant bottleneck. To address these inefficiencies, this project presents the design and implementation of an intelligent system for the automatic screening and ranking of resumes. The system puts forward an automated resume screening system leveraging Machine Learning (ML), and Natural Language Processing (NLP) to enhance recruitment efficiency and fairness. The system is architected to serve both recruiters, by enabling the ranking of multiple candidates, and applicants, by providing personalized feedback on their suitability for a role. A feature vector is engineered using a pre-trained Sentence-BERT (SBERT) model for semantic comparison, complemented by keyword overlap and document statistics. This vector is then used to train a Random Forest Classifier, which predicts a probabilistic suitability score for each candidate. A key contribution is a novel course recommendation engine for applicants, powered by a Large Language Model (GPT-4o mini). model. The entire system is deployed via an intuitive, dual-portal Gradio web application, providing a practical tool for both recruiters and candidates.
Rashi et al. (Fri,) studied this question.