The paper "LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations" explores the integration of large language models (LLMs) with graph neural networks (GNNs) to enhance recommendation systems. The authors address the challenge of leveraging edge information in graph data, which is critical for understanding complex node relationships. They propose a novel framework that combines the contextual representation capabilities of LLMs with the relationship extraction and analysis functions of GNNs. Specifically, they design a new prompt construction framework that integrates relational information into natural language expressions, aiding LLMs in grasping connectivity information within graph data. Additionally, they introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information. The evaluation on real-world datasets demonstrates the framework's ability to improve the relevance and quality of recommendation results. The contributions of the paper include an innovative method for combining LLMs with recommendation systems, a new prompt strategy, and a novel fusion method for edge information. The experimental results show that the proposed method significantly improves recommendation accuracy and personalization compared to traditional recommendation systems.The paper "LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations" explores the integration of large language models (LLMs) with graph neural networks (GNNs) to enhance recommendation systems. The authors address the challenge of leveraging edge information in graph data, which is critical for understanding complex node relationships. They propose a novel framework that combines the contextual representation capabilities of LLMs with the relationship extraction and analysis functions of GNNs. Specifically, they design a new prompt construction framework that integrates relational information into natural language expressions, aiding LLMs in grasping connectivity information within graph data. Additionally, they introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information. The evaluation on real-world datasets demonstrates the framework's ability to improve the relevance and quality of recommendation results. The contributions of the paper include an innovative method for combining LLMs with recommendation systems, a new prompt strategy, and a novel fusion method for edge information. The experimental results show that the proposed method significantly improves recommendation accuracy and personalization compared to traditional recommendation systems.