Comparative Analysis of Job Recommendation Filtering Techniques

  • 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/kpagc783

    Received date: May 22, 2025

    Accepted date: June 22, 2025

    Published date: September 19, 2025

  • Comparative Analysis, Precision, Recall, Job Recommendations, Hybrid Model, Collaborative Filtering, Fairness in AI, Graph Neural Networks

  • Abstract

    The objective of the paper is to present a technical and novel comparative study of job recommendation filtering techniques, addressing gaps in existing research. We evaluate Content-Based (CBF), Collaborative Filtering (CF), and Hybrid models, introducing a Graph-Enhanced Hybrid Model that improves skill-aware recommendations using Graph Neural Networks (GNNs). Our evaluation includes accuracy, diversity, novelty, and fairness metrics, demonstrating superior performance over baselines.

  • References

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  • How to Cite

    Soni, S. . ., & Shah , S. . (2025). Comparative Analysis of Job Recommendation Filtering Techniques. International Journal of Basic and Applied Sciences, 14(5), 730-734. https://doi.org/10.14419/kpagc783