Job Recommendation Systems Through AI and Machine Learning

  • Authors

    • Sachin Soni Research Scholar, Department of Computer Science, Kadi Sarva Vishwavidyalaya, Gandhinagar, Gujarat, India
    • Sanjay Shah Dean, Department of Computer Science, Kadi Sarva Vishwavidyalaya, Gandhinagar, Gujarat, India
    https://doi.org/10.14419/pr5w2v12

    Received date: May 22, 2025

    Accepted date: August 12, 2025

    Published date: September 19, 2025

  • AI, Hybrid, Job Recommendation, Deep Learning, Knowledge Graph, Reinforcement Learning, NLP, BERT, Cold Start, Resume Matching, Career Guid-ance

  • Abstract

    The explosive rise in digital recruitment platforms has generated the demand for smart systems that can make effective, personalized job recommendations. This paper introduces a new hybrid AI-driven job recommendation architecture that combines deep learning models (e.g., BERT for resume/job description embedding) with knowledge graph reasoning (to incorporate domain-specific semantics and relations) and reinforcement learning (for ongoing personalization). The system is tested against real-world job market data sets (e.g., Kaggle data set, Indeed, LinkedIn samples) and compared to baseline collaborative and content-based filtering methods. The outcomes show remarkable enhancements in recall, precision, and user satisfaction, which speak to the merits of fusing semantic comprehension with adaptive learning..

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  • How to Cite

    Soni , S. ., & Shah , S. . (2025). Job Recommendation Systems Through AI and Machine Learning. International Journal of Basic and Applied Sciences, 14(5), 735-742. https://doi.org/10.14419/pr5w2v12