Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and apply attention layers on these tokens to generate recommendations. However, by inheriting the standard attention mechanism, these methods focus on modeling token-level relations. This token-centric focus overlooks the item as the fundamental unit of recommendation, preventing existing methods from effectively capturing collaborative relations at the item level. In this work, we revisit the role of tokens in LLM-driven recommendation and categorize their relations into two types: (1) intra-item token relations, which present the content semantics of an item, e.g., name, color, and size; and (2) inter-item token relations, which encode collaborative relations across items. Building on these insights, we propose a novel framework with an item-aware attention mechanism (IAM) to enhance LLMs for recommendation. Specifically, IAM devises two complementary attention layers: (1) an intra-item attention layer, which restricts attention to tokens within the same item, modeling item content semantics; and (2) an inter-item attention layer, which attends exclusively to token relations across items, capturing item collaborative relations. Through this stacked design, IAM explicitly emphasizes items as the fundamental units in recommendation, enabling LLMs to effectively exploit item-level collaborative relations. Extensive experiments on several public datasets demonstrate the effectiveness of IAM in enhancing LLMs for personalized recommendation.

翻译：大语言模型（LLMs）近年来在推荐领域日益受到关注。现有基于LLM的方法通常将物品表示为词符序列，并对这些词符应用注意力层以生成推荐结果。然而，通过继承标准注意力机制，这些方法聚焦于建模词符级关系。这种以词符为中心的视角忽略了物品作为推荐的基本单元，导致现有方法无法有效捕捉物品层面的协同关系。在本工作中，我们重新审视词符在LLM驱动推荐中的作用，并将其关系分为两类：（1）物品内词符关系，呈现物品的内容语义（如名称、颜色、尺寸）；（2）物品间词符关系，编码跨物品的协同关系。基于这些洞见，我们提出一种新型框架，引入物品感知注意力机制（IAM）以增强LLMs的推荐能力。具体而言，IAM设计了两层互补的注意力层：（1）物品内注意力层，限制注意力仅作用于同一物品内的词符，建模物品内容语义；（2）物品间注意力层，仅关注跨物品的词符关系，捕捉物品协同关系。通过这种堆叠设计，IAM明确强调物品作为推荐的基本单元，使LLMs能够有效利用物品级协同关系。在多个公开数据集上的大量实验证明了IAM在增强LLMs实现个性化推荐方面的有效性。