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 s
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Iman Johary →
“STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling”
- LinkedLinked via arxiv author · 85%Guillaume Bied →
“STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling”
- LinkedLinked via arxiv author · 85%Alexandru C. Mara →
“STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling”
- LinkedLinked via arxiv author · 85%Tijl De Bie →
“STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling”