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DAAKSHAYANI. N.S1  \nMS. DIVYA PRABHA.G2, MS. KAVITHA.R3, PROF.DHARANIYA. N.G4  \n1 Student, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Affiliated to Anna University), Coimbatore, Tamil Nadu, India  \n1 Student Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Affiliated to Anna University), Coimbatore, Tamil Nadu, India  \n1 Student Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Affiliated to Anna University), Coimbatore, Tamil Nadu, India  \n2Professor, Department of Information Technology, Sri Shakthi Institute of Engineering and Technology (Affiliated to Anna University), Coimbatore, Tamil Nadu, India  \nABSTRACT  \nIn the modern recruitment process, Applicant Tracking Systems (ATS) play a crucial role in shortlisting candidates by automatically screening resumes against job descriptions. However, many qualified applicants fail to pass through these systems due to improper formatting, irrelevant keyword usage, and lack of semantic alignment. This paper presents a Smart ATS Resume Builder that uses Natural Language Processing (NLP) techniques powered by SpaCy for semantic parsing and Cosine Similarity Ranking for matching resumes to job descriptions. The proposed model analyzes key skills, entities, and contextual relevance between candidate resumes and target job postings. Experimental results demonstrate that the system enhances ATS compatibility, improves keyword optimization, and provides intelligent feedback to users for resume refinement. This tool aims to empower job seekers with data-driven insights to craft professional, AI-optimized resumes suitable for automated screening platforms.  \nKeywords: ATS Resume Builder, SpaCy, Cosine Similarity, Natural Language Processing, Job Matching  \nINTRODUCTION  \n1.1.Background  \nIn today’s digital era, job recruitment processes have significantly evolved due to the widespread use of online applications and automated systems. With thousands of candidates applying for similar positions, Applicant Tracking Systems (ATS) have become essential tools for recruiters to efficiently filter and shortlist resumes. However, many job seekers struggle to create resumes that are optimized for ATS algorithms, leading to rejection even when their qualifications match. Traditional resume formats often fail to align with specific job descriptions and keyword requirements, making it difficult for applicants to pass through the initial screening phase. To address this issue, artificial intelligence (AI) and Natural Language Processing (NLP) can be used to analyze both resumes and job postings.  \n1.2. Objective  \nThe main goal of this research is to design, develop, and evaluate a Smart ATS Resume Builder that leverages NLP and AI-driven similarity techniques to improve resume optimization and job matching accuracy. The specific objectives are:  \n􀁸 To preprocess and extract meaningful information from resumes and job descriptions using SpaCy.  \n􀁸 To implement TF-IDF vectorization and Cosine Similarity to rank resumes based on their relevance to given job descriptions.  \n􀁸 To enable automated AI-based resume generation and editing suggestions that align with industry requirements.  \n􀁸 To build an interactive web application that allows users to create, edit, and download ATS-optimized resumes in multiple languages.  \n􀁸 To enhance the recruitment process by improving job matching accuracy and reducing manual effort for  \n\n| IJRTI2510146 | International Journal for Research Trends and Innovation ([www.ijrti.org](www.ijrti.org)) | b370 |\n| --- | --- | --- |\n\nboth applicants and employers.  \n1.3. Scope  \nThis project focuses on developing an AI-based system that analyzes, scores, and optimizes resumes for Applicant Tracking Systems. The application allows users to input their career details or upload existing","cbCaie8AtOvIq92K","https://ap.wps.com/l/cbCaie8AtOvIq92K","pdf",422531,6,"English","# Abstract\n# Introduction\n## Background\n## Objective\n## Scope\n# Literature Survey","[{\"question\":\"What problem does the Smart ATS Resume Builder address?\",\"answer\":\"It addresses resume rejection in Applicant Tracking Systems caused by improper formatting, weak keyword usage, and poor semantic alignment with job descriptions.\"},{\"question\":\"How does the system match a resume to a job description?\",\"answer\":\"It uses NLP with SpaCy for semantic parsing and applies TF-IDF vectorization with Cosine Similarity to rank resume relevance to job descriptions.\"},{\"question\":\"What outcomes are expected from the proposed approach?\",\"answer\":\"The system aims to improve ATS compatibility, enhance keyword optimization, provide intelligent feedback for resume refinement, and reduce manual effort in recruitment.\"}]","Smart ATS Resume Builder using SpaCy and Cosine Similarity Ranking | PDF",1789091811]