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The University of Hong Kong
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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.
General-purpose LLMs can extract some signal directly from raw DNA sequences, but still struggle with complex genomic inference, highlighting a gap between their capabilities and the demands of real-world genomic analysis.
Forget static fusion: CAMMSR adaptively weights multimodal signals in sequential recommendations based on item category and user preferences, unlocking synergistic effects between modalities.
