The paper proposes an item-aware attention mechanism with intra-item and inter-item layers to let LLMs capture item-level collaborative relations instead of only token-level ones.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ReAd retrieves collaboratively similar items, builds an augmentation embedding via a lightweight module, and fuses it to refine sequential recommendation predictions, outperforming baselines on five datasets.
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Beyong Tokens: Item-aware Attention for LLM-based Recommendation
The paper proposes an item-aware attention mechanism with intra-item and inter-item layers to let LLMs capture item-level collaborative relations instead of only token-level ones.
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Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
ReAd retrieves collaboratively similar items, builds an augmentation embedding via a lightweight module, and fuses it to refine sequential recommendation predictions, outperforming baselines on five datasets.