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University of Science and Technology of China Hefei
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GALLM redefines sequential recommendation by seamlessly integrating collaborative signals into LLMs, achieving significant performance gains without extra complexity.
Relying on just the final layer of LLMs can lead to a 6.72% drop in recommendation performance鈥擨MFuse captures the full spectrum of semantic knowledge across layers for superior results.
