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@article{189601,
        author = {Revanth Y and Vinay Gouda D and Vinay Kumar H and Md Rafiq and Sarvar Begum},
        title = {AI-Powered Job Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7983-7989},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189601},
        abstract = {The rapid expansion of online recruitment platforms has led to information overload for job seekers and increased screening complexity for recruiters. Traditional job portals rely heavily on keyword-based matching, which often fails to provide personalized job recommendations. This paper presents an AI-powered job recommendation system that uses machine learning techniques to analyze user skills, qualifications, and preferences to generate relevant job suggestions. The system employs a Random Forest algorithm to match job seeker profiles with job requirements and rank suitable opportunities. The platform supports job seekers, companies, and administrators to streamline the recruitment process. The proposed system improves recommendation accuracy, reduces manual effort, and enhances overall recruitment efficiency.},
        keywords = {Artificial Intelligence, Job Recommendation System, Machine Learning, Random Forest, Recruitment Automation},
        month = {December},
        }