Enhanced Resume Analysis and Job Alignment using BERT

The manual resume screening and simple keyword matching used in current recruitment procedures frequently ignore the subtleties of candidate qualifications, leading to ineffective and biased candidate job matching. In this work, a novel automated approach is proposed that leverages cutting-edge natural language processing methods to expedite the hiring process. The method extracts and analyzes key information from resumes, such as skills, experience, and educational background, by combining the latest BERT embeddings, TF-IDF weighting, and K-Nearest Neighbours(KNN). The algorithm converts unstructured resume data into meaningful feature representations, thereby generating highly precise and relevant job suggestions. Furthermore, a user-friendly interface created with Streamlit facilitates real-time interactions between recruiters and candidates, enabling smooth resume uploading, processing, and job matching. Extensive experiments on diverse datasets demonstrate notable improvements in matching accuracy and processing time, underscoring the potential of the approach to reduce human bias and enhance hiring effectiveness. The manual resume screening and simple keyword matching used in current recruitment procedures frequently ignore the subtleties of candidate qualifications, leading to ineffective and biased candidate job matching. In this work, a novel automated approach is proposed that leverages cutting-edge natural language processing methods to expedite the hiring process. The method extracts and analyzes key information from resumes, such as skills, experience, and educational background, by combining the latest BERT embeddings, TFIDF weighting, and K-Nearest Neighbours(KNN). The algorithm converts unstructured resume data into meaningful feature representations, thereby generating highly precise and relevant job suggestions. Furthermore, a user-friendly interface created with Streamlit facilitates real-time interactions between recruiters and candidates, enabling smooth resume uploading, processing, and job matching. Extensive experiments on diverse datasets demonstrate notable improvements in matching accuracy and processing time, underscoring the potential of the approach to reduce human bias and enhance hiring effectiveness.