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 SBRS. Our framework adopts a two-stage architecture consisting of personalized information extraction and personalized information utilization. In the personalized information extraction stage, we construct a heterogeneous KG that integrates time-independent user-item interactions, item-item relations, item-feature associations, and external 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 heterogeneous 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（Session-Based Recommendation Systems, SBRS）旨在从交互序列中捕捉用户的短期意图。然而，匿名会话的常见假设限制了个性化性能，尤其是在稀疏或冷启动条件下。近年来，大语言模型（LLM）增强的推荐研究表明，LLM能够生成丰富的物品表征，但匿名会话的存在使得利用LLM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构知识图谱（KG）中推断的潜在用户画像，并将其集成至数据驱动的SBRS中。我们的框架采用两阶段架构，分别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，我们构建了一个异构KG，该图谱融合了时间无关的用户-物品交互、物品-物品关系、物品-特征关联以及DBpedia的外部元数据。随后，我们利用基于LLM导出的物品嵌入初始化的KG，通过异构深度图互信息（HDGI）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LLM导出的物品嵌入共同融入数据驱动SBRS的改进架构中，以生成相关物品候选集，随后通过基础序列模型进行重排序，从而强调短期会话意图。与先前仅依赖序列建模或基于文本的用户表征方法不同，我们的方法将用户画像建模基于从异构KG导出的结构化关系信号。在Amazon Books与Amazon Movies & TV数据集上的实验表明，与使用会话历史导出用户嵌入的序列模型相比，本方法持续取得性能提升。