  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.
