Comparative Analysis of Job Recommendation Filtering Techniques
About this article
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.
Keywords
- Comparative Analysis, Precision, Recall, Job Recommendations, Hybrid Model, Collaborative Filtering, Fairness in AI, Graph Neural Networks
References
[1] Job recommendation algorithms. AI Journal. Smith, A. (2020).
[2] Fairness in recommended systems. ACM. Lee, B. (2021).
[3] Huang, J., Xie, Z., Zhang, H., Yang, B., Di, C., & Huang, R. (2024). Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive Learning. Information, 15(9), 534. https://doi.org/10.3390/info15090534
[4] Zhang, X. (2025). Graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Do-main Information. Lecture Notes in Electrical Engineering, 147–161. https://doi.org/10.1007/978-981-96-2409-6_15
[5] Cen, Y., Jiang, S., Cai, W., & Cen, G. (2025). EGRec: a MOOCs course recommendation model based on knowledge graphs. Discover Applied Sciences, 7(6). https://doi.org/10.1007/s42452-025-07131-w
View more references (4)
[6] Ge, C., Wang, X., Zhang, Z., Qin, Y., Chen, H., Wu, H., Zhang, Y., Yang, Y., & Zhu, W. (2025). Behavior Importance-Aware Graph Neural Ar-chitecture Search for Cross-Domain Recommendation. ArXiv.org. https://arxiv.org/abs/2504.07102
[7] Carlo, A., Ferrara, A., Bufi, S., Malitesta, D., Noia, T. D., & Sciascio, E. D. (2023). KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. 576–587. https://doi.org/10.1145/3604915.3608804
[8] Reddy, J. S., & Sarode, K. (2023). Hybrid Recommendation System using Graph Neural Network and BERT Embeddings. ArXiv.org. https://arxiv.org/abs/2310.04878
[9] Tiwari, A., Dutta, H., & Khanizadeh, S. (2025). Heterogeneous Sequel-Aware Graph Neural Networks for Sequential Learning. ArXiv.org. https://arxiv.org/abs/2506.05625