Machine learning-powered skill-job matching recommendation system for vocational education

Skill-job alignment remains a significant challenge in vocational education, where students often graduate with limited guidance on career pathways matching their acquired competencies. The gap between vocational training outcomes and dynamic labor market demands underscores the need for intelligent systems that facilitate personalized employment recommendations. To address this, a Machine Learning-Powered Skill-Job Matching Recommendation System is proposed for vocational graduates, integrating a novel hybrid model named Scalable Slime Mould optimized-Adaptive Random Forest Tree (SSM-ARFT). The model utilizes a curated dataset combining vocational student profiles, academic performance, certifications, and job requirement metadata gathered from institutional databases and public employment platforms. Data preprocessing is performed using normalization techniques to ensure uniformity across varying data types. For feature extraction, Principal Component Analysis (PCA) is employed to identify the most influential attributes for job-role alignment. The core framework involves mapping students’ skill profiles to job attributes through multi-level filtering and learning stages. The proposed SSM-ARFT algorithm enhances the ARFT by introducing slime mold-inspired metaheuristics for dynamic feature selection and adaptive learning, ensuring robustness and scalability across varying datasets. This intelligent recommendation system helps guide vocational students toward employment options that are closely aligned with their competencies. The proposed method is implemented by using Python 3.10.1. The models were trained and tested with k-fold cross-validation data. The findings determine that the suggested model outperforms traditional methods in metrics such as precision, F1 score, recall, and accuracy, which range from 91% to 94%. The research concludes that the proposed model offers a practical, scalable solution for effective skill-job matching, thereby enhancing graduate employability in vocational sectors.