Resume Analyzer and Job Recommendation System

@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},
  }