In recent years, efforts have been made to use text information for better
user profiling and item characterization in recommendations. However, text
information can sometimes be of low quality, hindering its effectiveness for
real-world applications. With knowledge and reasoning capabilities capsuled in
Large Language Models (LLMs), utilizing LLMs emerges as a promising way for
description improvement. However, existing ways of prompting LLMs with raw
texts ignore structured knowledge of user-item interactions, which may lead to
hallucination problems like inconsistent description generation. To this end,
we propose a Graph-aware Convolutional LLM method to elicit LLMs to capture
high-order relations in the user-item graph. To adapt text-based LLMs with
structured graphs, We use the LLM as an aggregator in graph processing,
allowing it to understand graph-based information step by step. Specifically,
the LLM is required for description enhancement by exploring multi-hop
neighbors layer by layer, thereby propagating information progressively in the
graph. To enable LLMs to capture large-scale graph information, we break down
the description task into smaller parts, which drastically reduces the context
length of the token input with each step. Extensive experiments on three
real-world datasets show that our method consistently outperforms
state-of-the-art methods.

利用大型语言模型来改进描述文本的办法受到了研究者的关注，本论文提出了一种基于图卷积的大型语言模型方法，通过捕捉用户 - 物品图中的高阶关系来改进描述文本。在真实世界的数据集上进行了广泛的实验，结果表明我们的方法始终优于现有的最先进方法。