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This paper introduces TRWH, a novel framework that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) to enhance recommendation systems in sparse data environments. By combining LLM-generated textual profiles with heterogeneous graph structures through strategic random walk augmentation, TRWH effectively captures both structural and semantic signals. Experimental results on the Amazon Fashion and Beauty datasets show significant performance improvements, achieving reductions of up to 80.0% in RMSE and 52.6% in MAE, highlighting the framework's effectiveness in addressing the challenges of sparse recommendations.
TRWH achieves up to 80% reduction in RMSE for sparse recommendation tasks by seamlessly integrating LLMs with GNNs through innovative random walk strategies.
Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively. However, integrating their complementary strengths remains challenging, particularly in sparse settings where maintaining semantic precision is critical. We propose TRWH (Text-driven Random Walk Heterogeneous Graph Neural Network), a novel framework that fuses LLM-generated textual profiles with heterogeneous graph structures through strategic random walk augmentation. TRWH consists of three core components: (1) Embedding Creation, which produces user and item representations using both Word2Vec and LLM-based profiling; (2) a Heterogeneous Graph Neural Network (HeteroGNN) that propagates information across multi-relational edges; and (3) Random Walk-based Path Construction, which enriches sparse graphs with second-order user-user and item-item links. Experiments on the Amazon-2023 Fashion (2M users, 825K items) and Beauty (631K users, 112K items) datasets demonstrate that TRWH achieves substantial performance gains over state-of-the-art methods, including 80.0% RMSE and 52.6% MAE reductions on Fashion, and 25.7% and 10.8% improvements on Beauty. Notably, while random walks improve performance with traditional embeddings, they can dilute the nuanced representations learned by LLMs, underscoring the importance of adaptive integration strategies.