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Resume Analyzer and Job Recommendation System
Subject area: Science,Engineering and Technology · Area of research: Computer Science and Engineering
Abstract
Resume Analyzer and Job Recommendation System is an innovative system designed to address challenges in the recruitment process, such as managing the high volume of resumes and handling non-standardized formats. By leveraging advanced technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning algorithms, the platform streamlines candidate evaluation and job matching. It extracts essential information from unstructured resumes, including skills, education, and experience, and transforms it into structured data for accurate analysis. Using methods like Count Vectorization, Term Frequency-Inverse Document Frequency (TF-IDF), and Cosine Similarity, Resume Analyzer and Job Recommendation System ensures precise alignment between candidates and job descriptions. Additionally, the K-Nearest Neighbors (KNN) algorithm ranks relevant resumes for specific roles based on similarity scores. Beyond matching, the system provides personalized recommendations, such as courses and certifications, to enhance candidates' profiles and align them with industry expectations.
Keywords
Cosine Similarity, Count Vectorization, KNN, TF-IDF
References
[1] Saeed Ashrafi, Babak Majidi, “Efficient Resume-Based Re-Education for Career Recommendation”, IEEE Access, 2023.
[2] Ms. Y. Sowjanya, Mareddy Keerthana, “Smart Resume Analyzer”, IJRES Volume 11 Issue 3, 2023.
[3] Hanae Mgarbil, Mohamed Yassin Chkouri, “Towards a New Job Offers Recommendation System Based on the Candidate Resume”, IJCDS , 2023.
[4] Chirag Daryani, Gurneet Singh Chhabra, “AN AUTOMATED RESUME SCREENING SYSTEM USING NATURAL LANGUAGE PROCESSING”, ETIT, 2020.
[5] Ronak Surve, Noel Monteiro, “Job Analista”, IJARSCT, 2024.
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How to cite this paper
@article{1707439,
author = {K S Varshith Reddy, Kiran N J, Likhith Gowda T R, Mohammed Amanullah, Harini S},
title = {Resume Analyzer and Job Recommendation System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {282-287},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707439.pdf},
abstract = {Resume Analyzer and Job Recommendation System is an innovative system designed to address challenges in the recruitment process, such as managing the high volume of resumes and handling non-standardized formats. By leveraging advanced technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning algorithms, the platform streamlines candidate evaluation and job matching. It extracts essential information from unstructured resumes, including skills, education, and experience, and transforms it into structured data for accurate analysis. Using methods like Count Vectorization, Term Frequency-Inverse Document Frequency (TF-IDF), and Cosine Similarity, Resume Analyzer and Job Recommendation System ensures precise alignment between candidates and job descriptions. Additionally, the K-Nearest Neighbors (KNN) algorithm ranks relevant resumes for specific roles based on similarity scores. Beyond matching, the system provides personalized recommendations, such as courses and certifications, to enhance candidates' profiles and align them with industry expectations.},
keywords = {Cosine Similarity, Count Vectorization, KNN, TF-IDF},
month = {March},
}