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Grafting trainable components onto frozen teacher models reveals the hidden pitfalls of generalization in sequential recommenders, leading to substantial performance gains.
TRACER balances the Stability-Plasticity-Cognitivity trilemma, leading to a significant performance boost in continual recommendation systems.
Self-attention in recommendation systems overlooks crucial item relations, but PRISM's multi-perspective approach reveals hidden user preferences that boost performance.
Achieve ensemble-level sequential recommendation performance with a single network at inference time by distilling diversity from a modular ensemble during training.
Naive fine-tuning of VLMs for multimodal sequential recommendation causes catastrophic modality collapse, but can be fixed with gradient rebalancing and cross-modal regularization.
