Session-based recommendation systems (SBRS) aim to capture user's short-term intent from interaction sequences. However, the common assumption of anonymous sessions limits personalization, particularly under sparse or cold-start conditions. Recent advances in LLM-augmented recommendation have shown that LLMs can generate rich item representations, but modeling user personas with LLMs remains challenging due to anonymous sessions. In this work, we propose a persona-driven SBRS framework that explicitly models latent user personas inferred from a heterogeneous knowledge graph (KG) and integrates them into a data-driven recommendation pipeline.Our framework adopts a two-stage architecture consisting of personalized information extraction and personalized information utilization, inspired by recent chain-of-thought recommendation approaches. In the personalized information extraction stage, we construct a heterogeneous KG that integrates time-independent user-item, item-item, item-feature association, and metadata from DBpedia. We then learn latent user personas in an unsupervised manner using a Heterogeneous Deep Graph Infomax (HDGI) objective over a KG initialized with LLM-derived item embeddings. In the personalized information utilization stage, the learned persona representations together with LLM-derived item embeddings are incorporated into a modified architecture of data-driven SBRS to generate a candidate set of relevant items, followed by reranking using the base sequential model to emphasize short-term session intent. Unlike prior approaches that rely solely on sequence modeling or text-based user representations, our method grounds user persona modeling in structured relational signals derived from a KG. Experiments on Amazon Books and Amazon Movies & TV demonstrate that our approach consistently improves over sequential models with user embeddings derived using session history.

翻译：会话推荐系统（SBRS）旨在从交互序列中捕捉用户的短期意图。然而，匿名会话这一常见假设限制了个性化效果，尤其在数据稀疏或冷启动条件下。近年来，基于大型语言模型增强的推荐研究显示，语言模型可生成丰富的物品表征，但匿名会话特性使得使用语言模型建模用户画像仍具挑战性。本文提出一种基于用户画像的SBRS框架，该框架显式建模从异质知识图谱中推断的潜在用户画像，并将其整合至数据驱动的推荐流水线中。受近期思维链推荐方法的启发，我们的框架采用两阶段架构，包含个性化信息提取与个性化信息利用。在个性化信息提取阶段，我们构建融合时间无关的用户-物品、物品-物品、物品-特征关联以及DBpedia元数据的异质知识图谱。随后，利用语言模型初始化的物品嵌入初始化知识图谱，通过异质深度图互信息目标以无监督方式学习潜在用户画像。在个性化信息利用阶段，将学习到的画像表征与语言模型导出的物品嵌入共同输入改进的数据驱动SBRS架构，生成相关物品候选集，随后通过基础序列模型进行重排序以强化短期会话意图。与仅依赖序列建模或文本用户表征的先前方法不同，本文方法将用户画像建模建立在知识图谱提供的结构化关系信号之上。在Amazon Books与Amazon Movies & TV数据集上的实验表明，相较于基于会话历史提取用户嵌入的序列模型，本方法持续取得更优性能。