כאשר קורלציות מטעות: פרקטיקה חכמה לייצוג רגיונים עירוניים בעזרת נתונים רב-מוטיבטיביים

תקציר מקורי באנגליתarXiv:2609.15305v1 Announce Type: cross Abstract: Urban region representation learning commonly combines heterogeneous data sources, such as mobility flows, points of interest, and land-use information, to support tasks including mobility analysis, public safety forecasting, and service demand estimation. Existing multi-view methods typically improve region embeddings by strengthening interactions across views. However, such methods often overlook view-specific regional structures and may propagate correlations induced by shared latent factors, which can reduce the stability of downstream predictions. To overcome this major limitation, we propose CURE, a confounder-aware framework for multi-view urban region representation learning. CURE first encodes each view with its regional graph stru