The AI Resume Ranker using Deep Learning is an intelligent system designed to automate the candidate screening process by analyzing and ranking resumes based on their relevance to a given job description. The system accepts a job description and a collection of resumes as input, performs standard Natural Language Processing (NLP) preprocessing steps such as text cleaning, tokenization, and normalization, and converts the textual data into numerical representations using techniques like word embeddings and contextual embeddings from BERT. It employs multiple deep learning models, including LSTM, BiLSTM, CNN, and attention mechanisms, to capture sequential dependencies, contextual meaning, and important features within the text. These models collectively evaluate how well each resume matches the job requirements by computing a relevance score based on semantic similarity. The final output is a ranked list of candidates, enabling recruiters to quickly identify the most suitable applicants. This approach significantly reduces manual effort, improves accuracy in candidate selection, minimizes bias, and enhances the efficiency of the recruitment process, making it highly applicable in modern HR systems and job portals.
Vardhan et al. (2026) studied this question.