This comparative analysis evaluates job recommendation filtering techniques, highlighting a novel graph-enhanced hybrid model that improves accuracy and fairness.
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.
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Soni et al. (2025) studied this question.
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