With the rapid development of Large Language Models (LLMs), various explorations have arisen to utilize LLMs capability of context understanding on recommender systems. While pioneering strategies have primarily transformed traditional recommendation tasks into challenges of natural language generation, there has been a relative scarcity of exploration in the domain of session-based recommendation (SBR) due to its specificity. SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their ability to capture both the implicit and explicit relationships between adjacent behaviors. The structural nature of graphs contrasts with the essence of natural language, posing a significant adaptation gap for LLMs. In this paper, we introduce large language models with graphical Session-Based recommendation, named LLMGR, an effective framework that bridges the aforementioned gap by harmoniously integrating LLMs with Graph Neural Networks (GNNs) for SBR tasks. This integration seeks to leverage the complementary strengths of LLMs in natural language understanding and GNNs in relational data processing, leading to a more powerful session-based recommender system that can understand and recommend items within a session. Moreover, to endow the LLM with the capability to empower SBR tasks, we design a series of prompts for both auxiliary and major instruction tuning tasks. These prompts are crafted to assist the LLM in understanding graph-structured data and align textual information with nodes, effectively translating nuanced user interactions into a format that can be understood and utilized by LLM architectures. Extensive experiments on three real-world datasets demonstrate that LLMGR outperforms several competitive baselines, indicating its effectiveness in enhancing SBR tasks and its potential as a research direction for future exploration.

翻译：随着大型语言模型（LLMs）的快速发展，利用其在上下文理解方面的能力应用于推荐系统的探索日益增多。尽管开创性策略主要将传统推荐任务转化为自然语言生成的挑战，但受限于会话推荐（SBR）的特殊性，该领域的探索相对匮乏。图神经网络因其能够捕捉相邻行为之间隐含和显式关系的优势，长期主导着SBR领域并取得了诸多成功。然而，图结构数据的本质与自然语言特性存在差异，这使得LLMs面临显著的适应性鸿沟。本文提出集成大型语言模型的图式会话推荐框架LLMGR，通过将LLMs与图神经网络（GNNs）有机结合，有效弥合了这一鸿沟，用于解决SBR任务。这种集成旨在利用LLMs在自然语言理解方面的互补优势以及GNNs在关系数据处理方面的优势，从而构建更强大的会话推荐系统，使其能够理解并推荐会话中的项目。此外，为使LLMs具备赋能SBR任务的能力，我们设计了一系列用于辅助和主要指令调优任务的提示。这些提示旨在帮助LLMs理解图结构数据，并将文本信息与节点对齐，从而将细微的用户交互有效转化为LLM架构可理解并利用的格式。在三个真实数据集上的大量实验表明，LLMGR的性能优于多个竞争基线，验证了其在增强SBR任务方面的有效性，并为未来研究提供了潜在方向。