Zitao Guo

TOPT: TASK-ORIENTED PROMPT TUNING FOR URBAN REGION REPRESENTATION LEARNING

Learning effective region embeddings from heterogeneous urban data underpins key urban computing tasks (e.g., crime prediction, resource allocation). However, prevailing two-stage methods yield task-agnostic representations, decoupling them from downstream objectives. Recent prompt-based approaches…