Job Recommendation System

  • Authors

    • Ameya Parkar Department of MCA, VESIT, Mumbai, India
    • Mona Deshmukh Department of MCA, VESIT, Mumbai, India
    • Girish Deshmukh Department of Mechanical Engineering, AC Patil Institute of Technology, Navi Mumbai, India
    https://doi.org/10.14419/mhjxnq18

    Received date: June 11, 2025

    Accepted date: July 16, 2025

    Published date: July 20, 2025

  • Recommendation System; Hybrid Model; Machine Learning; Natural Language Processing; User Profile; Skill-based

  • Abstract

    Online recruitment platforms have led to a surge in job applications, creating challenges for companies in managing and reviewing them ‎efficiently. With the exponential growth of online job postings and user profiles, the challenge of matching the right job to the right candidate has become increasingly complex. Job recommendation systems aim to streamline this process by leveraging advanced algorithms to ‎suggest relevant job opportunities to job seekers based on their skills, experience, and preferences. Our proposed technique performs better ‎in terms of personalized job recommendations to different users based on their profiles‎.

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  • How to Cite

    Parkar , A. ., Deshmukh , M. ., & Deshmukh , G. . (2025). Job Recommendation System. International Journal of Basic and Applied Sciences, 14(3), 163-169. https://doi.org/10.14419/mhjxnq18