Large Language Models (LLMs) are increasingly prominent in the recommendation systems domain. Existing studies usually utilize in-context learning or supervised fine-tuning on task-specific data to align LLMs into recommendations. However, the substantial bias in semantic spaces between language processing tasks and recommendation tasks poses a nonnegligible challenge. Specifically, without the adequate capturing ability of collaborative information, existing modeling paradigms struggle to capture behavior patterns within community groups, leading to LLMs' ineffectiveness in discerning implicit interaction semantic in recommendation scenarios. To address this, we consider enhancing the learning capability of language model-driven recommendation models for structured data, specifically by utilizing interaction graphs rich in collaborative semantics. We propose a Graph-Aware Learning for Language Model-Driven Recommendations (GAL-Rec). GAL-Rec enhances the understanding of user-item collaborative semantics by imitating the intent of Graph Neural Networks (GNNs) to aggregate multi-hop information, thereby fully exploiting the substantial learning capacity of LLMs to independently address the complex graphs in the recommendation system. Sufficient experimental results on three real-world datasets demonstrate that GAL-Rec significantly enhances the comprehension of collaborative semantics, and improves recommendation performance.

翻译：大型语言模型在推荐系统领域日益突出。现有研究通常利用上下文学习或针对特定任务数据的监督微调来使语言模型适应推荐任务。然而，语言处理任务与推荐任务之间语义空间的显著偏差构成了不可忽视的挑战。具体而言，由于缺乏对协同信息的充分捕获能力，现有建模范式难以捕捉社群内部的行为模式，导致语言模型在推荐场景中无法有效辨识隐含的交互语义。为解决这一问题，我们考虑增强语言模型驱动推荐模型对结构化数据的学习能力，特别是利用富含协同语义的交互图。我们提出了面向语言模型驱动推荐的图感知学习方法。该方法通过模仿图神经网络聚合多跳信息的机制来增强对用户-项目协同语义的理解，从而充分利用语言模型强大的学习能力独立处理推荐系统中的复杂图结构。在三个真实数据集上的充分实验结果表明，该方法显著提升了对协同语义的理解能力，并有效改善了推荐性能。