Next Step: A Hybrid AI Framework for Demand-Driven Career Recommendation Using Skill Gap Quantification and Semantic Similarity

1. Alireza, A., Kirsty, K., Marian-Andrei, R. & Katarzyna, M., 2022. Skills Taught vs Skills Sought: Using Skills Analytics to Identify the
Gaps between Curriculum and Job Markets. Durham, International Educational Data Mining Society, pp. 538-542.
2. Baldi, M. M., Galuzzi, B. G., Messina, E. & Kaniadakis, G., 2026. Classification methods based on κ-logistic models. Mathematics and
Computers in Simulation (MATCOM), Volume 240, pp. 347-366.
3. Deepak, K. et al., 2023. Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives.
Frontiers in Big Data, Volume 6.
4. Evidently, A., 2025. 10 metrics to evaluate recommender and ranking systems. [Online] Availablat: https://www.evidentlyai.com/ranking-
metrics/evaluating-recommender-systems [Accessed 02 April 2026].
5. Fang, T. et al., 2025. Explainable person–job recommendations: challenges, approaches, and comparative analysis. Frontiers in
Artificial Intelligence, Volume 8.
6. Galuzzi, B., Giordani, I. & Candelieri, A., 2020. Hyperparameter optimization for recommender systems through Bayesian
optimization. Computational Management Science, 17(4), pp. 495-515.
7. Holland, J. L., 1997. Making Vocational Choices: A Theory of Vocational Personalities and Work Environments. 3rd ed. Odessa,
Florida: Psychological Assessment Resources.
8. Huang, X. & Baker, R., 2021. Skill-based Career Path Modeling and Recommendation. Cham, Springer, pp. 203-214. Januzaj, Y. & Luma,
A., 2022. Cosine Similarity – A Computing Approach to Match Similarity Between Higher Education Programs and Job Market Demands
Based on Maximum Number of Common Words. International Journal of Emerging Technologies in Learning (iJET), 17(12), pp. 258-
268.
9. Kainyu, F., Mwadulo, M. & Munialo, S., 2025. A hybrid recommender model for career pathway selection in competency-based
education. International Journal of Computer Applications, 187(2), pp. 15-22.
10. Mishra, R. & Rathi, S., 2022. Enhanced DSSM (deep semantic structure modelling) technique for job recommendation. Journal of
King Saud University - Computer and Information Sciences, 34(9), pp. 7790-7802.
11. Oschinski, M. & Thanh, N., 2022. Finding the Right Job: A Skills-Based Approach to Career Planning.. s.l.: Institute for Research on
Public Policy. Pranjali1121, 2024. AI Jobs Posting Trends from 2019–2023. [Online] Available at:
https://www.kaggle.com/datasets/pranjali1121/ai-jobs-posting-trends-from-2019-2023 Accessed 6 March 2026].
12. Purohit, D., Jayswal, M. & Muduli, A., 2021. The influences on career choice among commerce and management students. Cogent
Education, 8(1), pp. 1-18.
13. Salton, G. & McGill, M. J., 1983. Introduction to modern information retrieval. New York: MCGraw-Hill.
14. Sharma, R., 2024. Enhancing Job Recommendation Systems through Machine Learning: A Comprehensive Analysis of Skill Sync Job
Recommendation System. International Journal of Scientific Research & Engineering Trends, 10(3), pp. 741-744.
15. Udai, J., Daksh, J. & Aditya Raj, V., 2023. A Deep Learning Approach to Job Recommendation Analysis with NLP. International
Journal of Innovative Science and Research Technology, November.8(11).
16. Webb, M., 2020. The Impact of Artificial Intelligence on the Labor Market, Palo Alto, CA: Stanford Institute for Economic Policy
Research [15(SIEPR).
17. Yadalam, T. et al., 2020. Career Recommendation Systems using Content based Filtering. Coimbatore, India, IEEE, pp. 660-665.