Comparative Analysis of Job Recommendation Filtering Techniques - Soni, Sachin; Shah , Sanjay (International Journal of Basic and Applied Sciences, 2025)
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.
Keywords: Comparative Analysis, Precision, Recall, Job Recommendations, Hybrid Model, Collaborative Filtering, Fairness in AI, Graph Neural Networks