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Self-attention in recommendation systems overlooks crucial item relations, but PRISM's multi-perspective approach reveals hidden user preferences that boost performance.
Naive fine-tuning of VLMs for multimodal sequential recommendation causes catastrophic modality collapse, but can be fixed with gradient rebalancing and cross-modal regularization.
