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@article{191258,
        author = {M. Kanishca and V. Mageshwari},
        title = {RESUME ANALYZER WITH JOB RECOMMENDATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6032-6035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191258},
        abstract = {Despite the upsurge in recruitment platforms now present online for job recruitment tools, job seekers find it difficult to find a job that suits their skills, whereas employers are overwhelmed by piles of CVs on their desks. Manual screening is a frustrating task that calls for astute judgment on the part of the employer/employee. The Resume Analyzer cum Job Recommendation System bridges that gap through Natural Language Processing.
When the resume is uploaded, the major details are extricated, which include skills, qualifications, and work experience, and these are then matched with options that are predefined in job categories. It relies on TF-IDF to derive the attributes from the resume, with the help of which the exact job categories are located using the technique of cosine similarity. Client-side services are based on HTML/CSS, whereas the server is based on Flask/SQLite.},
        keywords = {Resume Analyzer, Job Recommendation System, Natural Language Processing, Skill Extraction, Flask (Python)},
        month = {January},
        }