让大模型学会看用户行为图谱，提升序列推荐效果
Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
- 构建文本与物品的协同图，用注意力偏置注入全局互动模式
- 在四个真实数据集上平均提升9.76%的命中率（HR@5）
- 无需额外图网络，轻量改造即可增强大模型推荐能力
大型语言模型（LLM）被广泛用于推荐系统，但其以语言为中心的预训练使其难以捕捉用户-物品交互中隐含的协同信号，而这些信号对个性化推荐至关重要。现有方法或引入外部推荐器生成的协同表示，或仅建模序列内依赖，限制了对全局协同模式的利用。为此，我们提出GALLM，一种面向序列推荐的图感知大模型框架。GALLM在文本标记和物品标记之间构建协同图，建模三类关系：文本-文本关系以保持语义依赖，物品-文本关系对齐物品标记与其文本描述，物品-物品关系来自全局物品共现模式。这些关系转化为轻量级可学习的注意力偏置，并融入LLM注意力机制，实现协同感知的标记交互，无需引入额外图编码器。在四个真实世界基准上的实验表明，GALLM在对比基线中表现最佳，平均在HR@5上超越最强基线9.76%。
原文摘要 · Abstract (English)
Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative signals implicit in user-item interactions, which are crucial for personalized recommendation. Existing methods either inject collaborative representations produced by external recommenders or model only intra-sequence dependencies, limiting their ability to exploit global collaborative patterns. To address this limitation, we propose GALLM, a graph-aware LLM framework for sequential recommendation. GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations: Text--Text relations for preserving semantic dependencies, Item--Text relations for aligning item tokens with their textual descriptions, and Item--Item relations derived from global item co-occurrence patterns. These relations are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder. Experiments on four real-world benchmarks show that GALLM achieves the best performance among the compared baselines, improving over the strongest baseline by 9.76\% on average in HR@5.
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