The purpose of this research is to create a personalized system called CARIA that suggests career recommendations based on students' competencies and the required skills in each career. 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. This research contributes to education in digital technology, digital media, and the workforce by providing employees with competencies that align with their needs.