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AI-Powered ATS: Resume Builder & Tracker
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV9I10-1717079
Abstract
The AI-Powered ATS: Resume Builder & Tracker is an advanced system developed to assist job seekers in creating professional, ATS-friendly resumes and efficiently managing their job applications. In today’s competitive job market, many companies use Applicant Tracking Systems (ATS) to filter resumes, making it essential for candidates to optimize their resumes with the right keywords and structure. This project uses Artificial Intelligence to analyze job descriptions and automatically suggest relevant keywords, skills, and content improvements to increase the chances of selection. The system allows users to input their personal, academic, and professional details, and then generates a well-structured resume tailored to specific job roles. It also provides resume scoring, skill gap analysis, and real-time suggestions for improvement. Additionally, the platform includes a job application tracker where users can record details such as company name, job role, application date, and current status. By integrating AI-based insights, the system helps users prioritize job opportunities and improve their overall job search strategy. It reduces manual effort, saves time, and enhances the effectiveness of applications. This solution is especially beneficial for students and fresh graduates who may lack experience in resume building and job tracking. Overall, the project aims to simplify and modernize the recruitment preparation process through intelligent automation and data-driven decision-making.
Keywords
Artificial Intelligence (AI), Applicant Tracking System (ATS), resume builder, resume optimization, keyword matching, Natural Language Processing (NLP), machine learning, job application tracking, resume scoring, skill gap analysis, automated resume generation, user dashboard, career management, and improved job selection chances to enhance the job search experience.
References
[1] Smith, J., et al. Automated Resume Screening using Machine Learning in Proceedings of IEEE International Conference on Data Mining (ICDM). 2020. Sorrento, Italy.
[2] Kumar, R., et al. Resume Information Extraction using Natural Language Processing in Proceedings of International Conference on Computational Linguistics (COLING). 2021. Barcelona, Spain.
[3] Lee, H., et al. Deep Learning Approaches for Recruitment Automation in Proceedings of AAAI Conference on Artificial Intelligence. 2022. Vancouver, Canada.
[4] Zhang, Y., et al. Intelligent Resume Parsing and Job Matching System in Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2019. Anchorage, USA.
[5] Patel, S., et al. AI-Based Talent Acquisition System using NLP Techniques in Proceedings of IEEE International Conference on Big Data. 2023. Sorrento, Italy.
[6] Brown, T., et al. Language Models are Few-Shot Learners in Proceedings of Advances in Neural Information Processing Systems (NeurIPS). 2020. Virtual Conference.
[7] Devlin, J., et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding in Proceedings of NAACL-HLT. 2019. Minneapolis, USA.
[8] Mikolov, T., et al. Efficient Estimation of Word Representations in Vector Space in Proceedings of International Conference on Learning Representations (ICLR). 2013. Scottsdale, USA.
[9] Vaswani, A., et al. Attention is All You Need in Proceedings of Advances in Neural Information Processing Systems (NeurIPS). 2017. Long Beach, USA.
[10] Lample, G., et al. Neural Architectures for Named Entity Recognition in Proceedings of NAACL-HLT. 2016. San Diego, USA.
How to cite this paper
@article{1717079,
author = {Deepthi Nair, Thasleem, Varsha, Vijay, Vikash; Vinodhini},
title = {AI-Powered ATS: Resume Builder & Tracker},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {5062-5068},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717079.pdf},
abstract = {The AI-Powered ATS: Resume Builder & Tracker is an advanced system developed to assist job seekers in creating professional, ATS-friendly resumes and efficiently managing their job applications. In today’s competitive job market, many companies use Applicant Tracking Systems (ATS) to filter resumes, making it essential for candidates to optimize their resumes with the right keywords and structure. This project uses Artificial Intelligence to analyze job descriptions and automatically suggest relevant keywords, skills, and content improvements to increase the chances of selection. The system allows users to input their personal, academic, and professional details, and then generates a well-structured resume tailored to specific job roles. It also provides resume scoring, skill gap analysis, and real-time suggestions for improvement. Additionally, the platform includes a job application tracker where users can record details such as company name, job role, application date, and current status. By integrating AI-based insights, the system helps users prioritize job opportunities and improve their overall job search strategy. It reduces manual effort, saves time, and enhances the effectiveness of applications. This solution is especially beneficial for students and fresh graduates who may lack experience in resume building and job tracking. Overall, the project aims to simplify and modernize the recruitment preparation process through intelligent automation and data-driven decision-making.},
keywords = {Artificial Intelligence (AI), Applicant Tracking System (ATS), resume builder, resume optimization, keyword matching, Natural Language Processing (NLP), machine learning, job application tracking, resume scoring, skill gap analysis, automated resume generation, user dashboard, career management, and improved job selection chances to enhance the job search experience.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1717079}
}