Behavior Graphs Beat Static User Profiles

Modeling user behavior as temporally evolving action graphs captures predictive signals static snapshots miss. Three core techniques, velocity, depth, and friction, quantify behavioral nuance from event streams. Zero-inflated exponential-family embeddings handle sparse data, while frequent sub-trajectory mining reduces sequence complexity for better predictive features.