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@article{198868,
        author = {Jayesh Sayankar and Sahil Barsagade and Asst.Prof. S.S. Ganorkar and Vishal Sitewar},
        title = {Career Navigator: AI-Driven Resume Screening & Job Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11798-11803},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198868},
        abstract = {In the era of digital transformation, recruitment processes are evolving rapidly to handle the growing number of job applications. Traditional resume screening methods are manual, time-consuming, and prone to human bias, making them inefficient for large-scale hiring. This paper presents an intelligent system, Career Navigator, which automates resume screening and job recommendation using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) techniques.
The system extracts structured information from unstructured resumes, identifies key features such as skills, education, and experience, and classifies candidates into relevant job categories. Furthermore, it uses similarity-based techniques to recommend suitable job roles. The proposed system enhances recruitment efficiency, reduces processing time, and ensures fair evaluation of candidates. Experimental analysis shows that the system achieves reliable performance in classification and recommendation tasks, making it suitable for real-world applications.},
        keywords = {Artificial Intelligence, Resume Screening, Job Recommendation, Machine Learning, Natural Language Processing, TF-IDF, Cosine Similarity, Recruitment Automation, Data Mining.},
        month = {April},
        }