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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.
GNN performance on heterophilic graphs suffers because of inductive subgraphs acting as spurious shortcuts, a problem that can be solved by causally disentangling these subgraphs.
By dynamically weighting historical interactions, TIPS lets sequential recommenders see past the biases of what users *actually* clicked, revealing what they *would* have clicked.
