Hybrid Career Path Recommendation System for Computer Science Students: Integrating Academic Profiles and Personal Skills with a Content–Collaborative Model

Career decision-making remains a critical challenge for computer science students, particularly within rapidly evolving labor markets such as Saudi Arabia. Traditional guidance approaches often rely on subjective advice or limited academic indicators, which frequently result in mismatched career choices and reduced employability. To address this gap, we propose a hybrid career recommendation system that integrates academic performance, technical and soft skills, and personal preferences. The proposed system combines artificial neural network (ANN) prediction, content-based career-profile matching, and collaborative peer similarity through weighted score fusion to generate ranked career path recommendations. The content-based component employs a similarity function (cosine similarity) to measure the alignment between the student’s attributes and each career profile. The collaborative component applies a k-Nearest Neighbors model to capture interaction-like signals from peer profiles. The ANN learns complex nonlinear relationships between student features and career outcomes, enabling the system to generate predictive probabilities for each career path by applying the TensorFlow framework for multi-class classification of student data. Final recommendations are produced through a weighted fusion of models. An experimental evaluation of 400 student records using stratified macro-level evaluation metrics demonstrates robust performance, achieving an accuracy of 93.75%, precision of 0.94, recall of 0.94, and F1-score of 0.94. The system provides Top-k-ranked career suggestions, along with interpretable feature importance, enhancing transparency for academic advisors and students. The results highlight the effectiveness of hybrid recommenders in mitigating cold-start and sparsity problems while capturing multidimensional student attributes. This study contributes a reproducible methodology and implementation pipeline that can be extended to real institutional contexts. Future work will include expansion to more university disciplines and integration with academic consulting platforms to support data-driven career planning.