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Predicting Career Transitions: Semantic Retrieval and Ontology-Based Signals for Career Transition Prediction
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Resumo(s)
Career transitions are a central feature of labor market dynamics, yet predicting plausible occupational moves remains challenging. Advances in natural language processing have made it possible to compare career histories with occupation descriptions at scale. However, purely semantic approaches often struggle to capture structured labor market knowledge and provide limited transparency regarding why certain occupations are recommended. This thesis investigates whether ontologybased skill signals can enhance semantic retrieval for career transition prediction within large occupational taxonomies. The task is formulated as an occupation-ranking problem and implemented using a two-stage retrieval–reranking framework. Experiments are conducted on a large-scale dataset of career trajectories aligned with the ESCO taxonomy, comprising thousands of occupations and skill annotations. A transformer-based bi-encoder first retrieves candidate occupations by embedding CV histories and occupation descriptions into a shared semantic space. The resulting candidate set is then reranked using features derived from the ESCO ontology that capture different aspects of skill compatibility. Both hybrid scoring strategies and lightweight learned rerankers are evaluated. The results indicate that ESCO-based skill signals consistently improve ranking performance over semantic retrieval alone, particularly when feature interactions are modeled. The best-performing model, based on XGBoost, achieves approximately a 58% relative improvement in Recall@10 on the test set compared to the semantic baseline. While semantic similarity effectively identifies conceptually related occupations, skill-based features provide stronger discrimination within the candidate set. Overall, combining semantic retrieval with structured skill knowledge provides a practical and interpretable approach for improving career transition prediction in large occupational taxonomies.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Career Transition Prediction Semantic Retrieval Ontology-Based Skill Modeling ESCO Learning-to-Rank