HireLoop is a custom machine learning and natural language processing based resume tailoring and ATS optimization platform developed as a B.Tech capstone project at GLS University. The system accepts a generic resume and a target job description, then performs resume-job matching, ATS score prediction, skill-gap analysis, resume reconstruction, and cover letter generation using custom-built ML and NLP models. HireLoop uses TF-IDF vectorization, cosine similarity, keyword extraction, named entity recognition, and rule-based resume optimization techniques to improve ATS compatibility while preserving the original information in the resume. Unlike existing tools, the platform does not depend on external generative AI APIs or third-party resume-writing services. Instead, it uses internally developed models and rule-based methods to provide transparent and low-cost resume optimization. This upload contains the IEEE-style research paper, system architecture, experimental results, and supporting material for the HireLoop project.
Devasya Gupta (Mon,) studied this question.