STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling
Career paths encode decades of skill acquisition, role transitions, and educational investment, and understanding them at scale underpins workforce planning, labor market policy, and job recommendation. Resumes are a rich source of information about career paths: they contain detailed descriptions of work experience, education, and skills. Yet their unstructured, heterogeneous, and multilingual nature has long prevented large-scale systematic analysis. With the advent of large language models (LLMs), it is now possible to source rich career trajectory data containing temporal and educational signals from unstructured resumes, enabling new opportunities for career-path recommendation. Expl...
| Authors | Iman Johary, Guillaume Bied, Alexandru C. Mara et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 July 2026 |
| Last updated at source | 15 July 2026 |
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