标题： HetGCoT-Rec：异构图增强的思维链大模型推理用于期刊推荐 显示英文标题

标题： HetGCoT-Rec: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Journal Recommendation

摘要： 学术期刊推荐需要有效结合对学术网络的结构理解与可解释的推荐。 虽然图神经网络（GNNs）和大语言模型（LLMs）在其各自领域表现出色，但当前方法往往无法在推理层面实现真正的整合。 我们提出了HetGCoT-Rec，一种通过思维链推理深度整合异构图变换器与LLMs的框架。 我们的框架具有两项关键技术创新：（1）一种结构感知机制，将异构图神经网络学习到的子图信息转换为自然语言上下文，利用预定义的元路径来捕捉学术关系，以及（2）一种多步骤推理策略，系统地将图生成的上下文嵌入到LLM的分阶段推理过程中。 在从OpenAlex收集的数据集上的实验表明，我们的方法显著优于基线方法，达到了96.48%的命中率和92.21%的H@1准确率。 此外，我们验证了该框架在不同LLM架构中的适应性，展示了推荐准确性和解释质量的一致性提升。 我们的工作展示了一种有效的方法，将图结构推理与语言模型结合，用于可解释的学术场所推荐。 显示英文摘要

摘要： Academic journal recommendation requires effectively combining structural understanding of scholarly networks with interpretable recommendations. While graph neural networks (GNNs) and large language models (LLMs) excel in their respective domains, current approaches often fail to achieve true integration at the reasoning level. We propose HetGCoT-Rec, a framework that deeply integrates heterogeneous graph transformer with LLMs through chain-of-thought reasoning. Our framework features two key technical innovations: (1) a structure-aware mechanism that transforms heterogeneous graph neural network learned subgraph information into natural language contexts, utilizing predefined metapaths to capture academic relationships, and (2) a multi-step reasoning strategy that systematically embeds graph-derived contexts into the LLM's stage-wise reasoning process. Experiments on a dataset collected from OpenAlex demonstrate that our approach significantly outperforms baseline methods, achieving 96.48% Hit rate and 92.21% H@1 accuracy. Furthermore, we validate the framework's adaptability across different LLM architectures, showing consistent improvements in both recommendation accuracy and explanation quality. Our work demonstrates an effective approach for combining graph-structured reasoning with language models for interpretable academic venue recommendations.