Multimodal Matching of Jobs and VET Courses in Portugal: Integrating Semantic and Geospatial Analysis
Year: 2026
Type: dissertation
Abstract: Aligning Vocational Education and Training (VET) with labor market demands is critical for economic development and workforce readiness. However, accurately mapping educational course syllabi to real-world job descriptions remains challenging, particularly in data-sparse institutional environments. Traditional retrieval methods relying on exact keyword matching suffer from vocabulary mismatch, while modern Cross-Encoder architectures capture deep semantic context but introduce prohibitive quadratic computational complexity. To address these bottlenecks, this thesis proposes a highly scalable recommendation pipeline leveraging Bi-Encoder architectures (Siamese Networks). Independently mapping Portuguese VET courses and job descriptions into a shared dense vector space enables rapid and efficient semantic matching via cosine similarity. Furthermore, to overcome prohibitive manual annotation costs, the study introduces an "LLM-as-a-Judge" methodology to evaluate semantic alignment on a nuanced 4-point scale, rigorously validated against a humanaudited, stratified sample across five key economic sectors. Benchmarking multiple embedding models across varied input normalization strategies revealed that the Language-agnostic BERT Sentence Embedding (LaBSE) model, paired with spaCybased syntactic keyword extraction, achieved the highest performance, yielding a peak Mean Normalized Discounted Cumulative Gain (MNDCG)@10 of 0.7873 and a Mean Precision (MP@10) of 0.464. Notably, this syntactic keyword preprocessing consistently outperformed both raw text inputs and generative LLM-based normalization. Ultimately, this research provides a robust, automated framework for vocational recommendation, offering institutions a highly efficient, data-driven tool to enhance the alignment between educational offerings and modern labor market needs.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G6143010816
OpenAlex ID: W7171758156