GaCLLM: Large Language Model with Graph Convolution for Recommendation

Abstract

Leveraging auxiliary textual data can help with user profiling and item characterization in recommender systems (RSs). However, incomplete user and item descriptions limit the potential of textual information. To this end, we propose a graph-aware convolutional large language model (LLM) method, eliciting LLMs to summarize from a high-order interaction graph to generate fine-grained descriptions for users and items. We focus on two challenges in this paper: 1) the incompatibility between structural graph and text-aware LLMs. Although some work converts graph data into textual form using templates and sampling strategies, they limit the LLMs’ ability to maintain a global perspective on graphs for LLM reasoning. 2) the limitation of LLMs’ capability for long context. Simply describing a large-scale graph in a textual format makes LLM struggle to robustly comprehend information from lengthy contextual inputs. To bridge the gap between graph structures and LLMs, we employ the LLM as an aggregator for the graph convolution process, eliciting it to aggregate and refine the graph-based information iteratively. To mitigate the information overload associated with large-scale graphs, we segment the graph processing into manageable steps, progressively incorporating multi-hop information in a least-to-most manner. Experiments on three real-world datasets demonstrate that our method consistently outperforms state-of-the-art approaches.