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IEEE/ICACT20260013 Slide.05        [Big Slide]       [YouTube]	Oral Presentation
	This slide summarizes the key contributions of our work. First, we develop a Bangladesh-specific job–candidate dataset constructed from local job postings and graduate skill profiles, ensuring that the data accurately reflects real market characteristics. Second, we introduce an age- and career-stage-aware job-matching approach. By incorporating candidate age and experience level, the system improves the relevance and practical feasibility of job recommendations. Third, we apply a machine-learning-driven job-ranking framework. We systematically evaluate Decision Tree, Logistic Regression, and Naïve Bayes models to generate personalized job rankings. Finally, we conduct an empirical analysis across multiple feature sizes to examine how feature dimensionality affects recommendation accuracy and overall system performance.

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