[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-261213-105":53,"doc-detail-261213-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","ai-resume-analyzer-and-job-description-matcher-design-and-implementation","AI Resume Analyzer and Job Description Matcher - Design and Implementation","","This paper presents the design and implementation of an AI-based Resume Analyzer and Job Description Matcher system that automates candidate evaluation by comparing resumes with job requirements. Using Natural Language Processing and Machine Learning, the system extracts skills, experience, and education from resumes, then matches them against job descriptions to compute a match score. It highlights missing skills and delivers improvement suggestions, leveraging Python, spaCy, TF-IDF, cosine similarity, and optional LLM-based feedback. Experimental observations indicate improved recruitment efficiency, more accurate candidate-job matching, reduced manual screening effort, and stronger decision support for recruiters while helping job seekers refine their resumes.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":16,"@type":70,"position":76},"https://docshare.wps.com/template/resumes/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/ai-resume-analyzer-and-job-description-matcher-design-and-implementation/261213/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/ai-resume-analyzer-and-job-description-matcher-design-and-implementation/261213.png","ImageObject",442,249,{"name":88,"@type":89},"Gloria","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-12","2026-09-14",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"How does the AI Resume Analyzer match a resume to a job description?","Question",{"text":108,"@type":109},"It extracts key information such as skills, education, and experience from both the resume and the job description, then calculates similarity using TF-IDF and cosine similarity to produce a match score.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What outputs does the system generate for recruiters and job seekers?",{"text":113,"@type":109},"The system provides a match score, highlights missing skills, and offers improvement suggestions, which helps recruiters shortlist candidates and helps job seekers strengthen their resumes.",{"name":115,"@type":106,"acceptedAnswer":116},"Which tools and techniques does the proposed system use?",{"text":117,"@type":109},"It uses Python and spaCy for text processing, TF-IDF to convert text into numerical features, and cosine similarity for matching; it also allows optional LLMs for more intelligent feedback.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},261213,1790515758,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":15,"category_name":16,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":135,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":79},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","AI RESUME ANALYZER AND JOB DESCRIPTION MATCHER  \n¹Mohd Afzal Chaudhary, ²Mehtab Khan, ³Imtiyazuddin Qureshi, ⁴Zameer Khan,5Dhanashree  \nKangane  \n¹ Student, ² Student, ³ Student, ⁴ Student, ⁵Project Guide  \nDepartment of ECS, Rizvi College of Engineering, Bandra, Mumbai, India  \nAbstract :- This paper presents the design and implementation of an AI-based Resume Analyzer and Job Description Matcher system that automates candidate evaluation by comparing resumes with job requirements. The system leverages Natural Language Processing (NLP) and Machine Learning (ML) techniques to extract relevant information such as skills, experience, and education from resumes and match them against job descriptions. The proposed system calculates a match score, highlights missing skills, and provides improvement suggestions. The solution uses tools such as Python, spaCy, TF-IDF, and cosine similarity, along with optional Large Language Models (LLMs) for intelligent feedback. Experimental results show improved efficiency in recruitment processes and accurate candidate-job matching. The system reduces manual screening effort and enhances decision-making.  \nIndex Terms—Resume Analysis, NLP, Machine Learning, Job Matching, TF-IDF, Cosine Similarity, AI Recruitment  \nI. INTRODUCTION  \nThe increasing number of job applications in modern recruitment has made manual resume screening a difficult and time-consuming task for recruiters. Organizations often receive hundreds of resumes for a single job position, making it challenging to quickly identify the most suitable candidates while also avoiding human bias and errors. With advancements in Artificial Intelligence and Natural Language Processing, automated systems can efficiently analyze textual information and support the recruitment process. An AI Resume Analyzer and Job Description Matcher is designed to automatically evaluate resumes by comparing their content with the requirements mentioned in a job description. The system extracts key information such as skills, education, and experience from both documents and calculates their similarity using techniques like Term Frequency–Inverse Document Frequency and Cosine Similarity. By generating a match score and identifying missing skills, the system helps recruiters shortlist qualified candidates more efficiently while also providing useful feedback to job seekers for improving their resumes. This approach enhances the accuracy, speed, and effectiveness of the recruitment process.  \nII. RELATED WORK  \nRecent advancements in intelligent recruitment systems have been driven by the integration of Artificial Intelligence, Machine Learning, and Natural Language Processing to automate resume screening and candidate evaluation processes. Many researchers have explored automated systems that analyze resumes and compare them with job descriptions to assist recruiters in identifying suitable candidates more efficiently.  \nA study on AI-based resume screening systems highlights that NLP techniques can effectively extract relevant information such as skills, qualifications, and work experience from resumes. These systems often use text processing techniques and similarity measures to match candidate profiles with job requirements, improving the speed and accuracy of recruitment decisions. The research also demonstrates that automated resume analysis can significantly reduce the workload of human resource professionals while maintaining high evaluation consistency.  \nAnother research work presents a systematic review of intelligent recruitment systems that combine machine learning algorithms with textual feature extraction methods. The study emphasizes the growing use of document similarity techniques such as Term Frequency–Inverse Document Frequency and Cosine Similarity to measure how closely a resume matches a given job description. These techniques help convert textual information into numerical vectors that can be compared computationally to determine relevance","cbCaiqlwJf2Bhoe0","https://ap.wps.com/l/cbCaiqlwJf2Bhoe0","pdf",1673472,9,10,"English","# Abstract\n# Introduction\n# Related Work\n# Methodology","[{\"question\":\"How does the AI Resume Analyzer match a resume to a job description?\",\"answer\":\"It extracts key information such as skills, education, and experience from both the resume and the job description, then calculates similarity using TF-IDF and cosine similarity to produce a match score.\"},{\"question\":\"What outputs does the system generate for recruiters and job seekers?\",\"answer\":\"The system provides a match score, highlights missing skills, and offers improvement suggestions, which helps recruiters shortlist candidates and helps job seekers strengthen their resumes.\"},{\"question\":\"Which tools and techniques does the proposed system use?\",\"answer\":\"It uses Python and spaCy for text processing, TF-IDF to convert text into numerical features, and cosine similarity for matching; it also allows optional LLMs for more intelligent feedback.\"}]","AI Resume Analyzer and Job Description Matcher - Design and Implementation | PDF",1789350375]