NLP-Powered Resume Screening and Ranking System

Indeed, the system employs Natural Language Processing (NLP) techniques to identify the main features in the resume, including skills, education, and work experience. It then compares these features with the job description by means of semantic similarity methods, such as TF-IDF (Term Frequency Inverse Document Frequency) and BERT (Bidirectional Encoder Representations from Transformers) embeddings. The ranking engine uses weighted scoring algorithms that prioritize the relevancy of resumes. Further, the system also needs the user to upload their resume and job description through the web-based interface where the output will display that a listing has been generated and is listed to be gone through with the candidates. They evaluate the performance of the system by means of evaluation metrics such as precision, recall, F1-score, and Mean Reciprocal Rank (MRR). In their results, it shows that BERT based semantic matching has proved better than the conventional keyword-based methods as far as accuracy and efficiency in screening resumes is concerned. What makes the current study noteworthy is that artificial intelligence (AI) could help improve recruiting automation and point to ways of streamlining the hiring process. This will bring significant reductions to bias and further improve the fairness and effectiveness of selecting a candidate. Further work contemplates multi-lingual support, domain-specific customizations, and integration of the system to Applicant Tracking Systems (ATS) to increase the importance of this study in real-world terms.