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IEEE/ICACT20260013 Slide.10        [Big Slide]       [YouTube]	Oral Presentation
	This slide describes the machine learning models used for job ranking. First, we use a Decision Tree model, which is a rule-based approach capable of capturing non-linear decision paths while maintaining clear interpretability. Second, Logistic Regression is applied as a linear probabilistic model that provides stable, consistent, and explainable job ranking. Finally, we use a Naïve Bayes classifier, which is a probabilistic model based on the assumption of conditional independence among features. Together, these models allow us to compare interpretability, stability, and probabilistic reasoning in personalized job recommendation.

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