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The focus of this study is on digital technology and digital media careers. The personalized career recommender system uses a novel similarity measure called modified Euclidean similarity to evaluate its performance and compare it with other similarity measures, machine learning, and GPT-4 techniques. The experimental results showed that modified Euclidean similarity achieved a precision@10 score of 0.83, which outperformed other techniques. The main objective of CARIA is to provide students with suitable career paths and conduct a competency gap analysis. This helps students choose a career path that fits their abilities. 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