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.

通过使用充满协作语义的交互图，我们提出了一种名为 GAL-Rec 的方法，通过模仿图神经网络（GNNs）的意图来提高语言模型驱动的推荐模型对用户 - 物品协作语义的理解能力，从而充分利用大型语言模型的学习能力，独立解决推荐系统中的复杂图形，实验证明 GAL-Rec 显著增强了协作语义的理解能力，并提高了推荐性能。