LLMGCL: Graph Contrastive Learning with Large Language Models for Recommendation

Shiqi Ge, Shunmei Meng, Jielong Zhou, Lianyong Qi, Xiaolong Xu, Xuyun Zhang

Abstract: Contrastive Learning (CL) has recently achieved significant progress in the field of recommender systems, as it leverages supervision signals from raw data to mitigate the issue of data sparsity. However, most existing methods rely on random or heuristic data augmentation strategies, which often disrupt the intrinsic structural relationships in the graph and introduce spurious connections, leading to suboptimal representations for recommendation tasks. To address these challenges, we propose a novel graph contrastive learning model based on Large Language Model (LLM), named LLMGCL, which leverages LLM for both embedding augmentation and graph augmentation, and incorporates inter-layer contrastive learning to improve recommendation performance. Specifically, the proposed method first designs an LLM-driven embedding augmentation strategy, where item embeddings pre-trained by LLM are aggregated to construct user embeddings, enabling contrastive views to retain richer semantic relationships. Furthermore, to enhance the structural integrity of the graph while minimizing noise, we develop two targeted graph augmentation strategies powered by LLM: one leverages LLM to identify high-confidence user-item interactions from historical behaviors to refine observed preferences, while the other infers potential but unobserved interactions to reinforce graph connectivity. Finally, we design an inter-layer contrastive learning module that aligns representations across different Graph Neural Network layers, effectively mitigating the over-smoothing effect and enhancing feature discrimination. These combined strategies significantly improve recommendation quality, as demonstrated by extensive experiments on public datasets.

External IDs:dblp:conf/icws/GeMZQXZ25