AI 中文总结
针对LLM增强型序列推荐中仅用最后一层表示的缺陷，提出IMFuse方法，通过实例感知多层融合策略生成个性化语义表示，在四个数据集上平均相对提升6.72%且开销有限。
AI 中文摘要
大语言模型（LLM）的近期进展通过将丰富的物品文本信息编码成语义表示，显著提升了序列推荐的性能。但现有方法通常依赖LLM的最后一层隐藏状态，忽略了其他层中可能有用的语义信号。通过实证分析，我们揭示了这种做法的局限性：最后一层表示常出现维度坍缩，而中间层则保留了互补的、由粗到细的语义知识。此外，我们观察到不同物品表现出异构的逐层表示演化，使得统一的层选择并非最优。为弥合这一差距，我们提出IMFuse，一种专为LLM增强型推荐设计的实例感知多层融合策略。IMFuse不依赖单一层，而是通过学习全局维度级层偏好以捕捉通用语义贡献，自适应聚合多层语义信息。为解决物品级异构性，IMFuse引入实例感知专家调制机制，动态调整这些全局偏好，生成个性化的、物品特有的语义表示。在四个真实世界数据集上的大量实验证明了IMFuse的有效性，它始终优于最先进的基线方法，平均相对提升达6.72%，同时引入的参数和计算开销有限。
英文摘要
Recent advancements in Large Language Models (LLMs) have significantly enhanced sequential recommendation by encoding rich item textual information into semantic representations. However, existing methods typically rely on the final-layer hidden states of LLMs, overlooking potentially useful semantic signals encoded in other layers. Through empirical analysis, we reveal the limitations of this practice: final-layer representations often suffer from dimensional collapse, whereas intermediate layers preserve complementary, coarse-to-fine semantic knowledge. Furthermore, we observe that different items exhibit heterogeneous layer-wise representation evolution, making a uniform layer selection sub-optimal. To bridge this gap, we propose IMFuse, an instance-aware multi-layer fusion strategy designed for LLM-enhanced recommendation. Instead of relying on a single layer, IMFuse adaptively aggregates multi-layer semantic information by learning global dimension-wise layer preferences to capture general semantic contributions. To address item-level heterogeneity, IMFuse introduces an instance-aware expert modulation mechanism that dynamically adjusts these global preferences, generating personalized, item-specific semantic representations. Extensive experiments across four real-world datasets demonstrate the effectiveness of IMFuse. It consistently outperforms state-of-the-art baselines with an average relative improvement of 6.72%, while introducing limited parameter and computational overhead.
Comments12 pages, 5 figures, and 9 tables. Yuheng Zheng and Yu Cui contributed equally. Jiawei Chen is the corresponding author
