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By recalibrating temporal signals based on item-specific interval distributions, TRUST achieves superior recommendation accuracy, challenging the one-size-fits-all approach in session-based systems.
S2-CAR reveals that dynamic segmentation based on latent energy states can dramatically improve recommendation accuracy by aligning with true user intent boundaries.
