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The University of Hong Kong
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NTCF achieves higher performance by adapting propagation depth to local connectivity, revealing that traditional methods may overlook critical node-specific dynamics.
DEFRAG narrows the accuracy gap between small and large language models while slashing costs by up to 98.4%, revolutionizing edge-based AI deployment.
A single high-quality item-item graph can significantly boost multimodal recommendation performance, especially in challenging cold-start situations.
Dramatically improve multimodal recommendation accuracy without any training by initializing user embeddings with item modality features and user cluster information.
LLM agents can be made more reliable by structurally verifying their internal reasoning, rather than relying on consensus which conflates agreement with faithfulness.
Forget static fusion: CAMMSR adaptively weights multimodal signals in sequential recommendations based on item category and user preferences, unlocking synergistic effects between modalities.
