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.

通过将大型语言模型（LLMs）与图神经网络（GNNs）有机地结合为 SBR（基于会话的推荐）任务设计了 LLMGR 框架，旨在提高会话内的项目理解和推荐能力，通过辅助和主要指令调整任务的提示来赋予 LLM 处理图结构数据的能力，通过在三个实际数据集上进行广泛实验，证明 LLMGR 在提升 SBR 任务方面优于竞争性基线模型，显示其对未来研究的潜力。