Recent advances in recommendation with large language models (LLMs) often rely on either commonsense augmentation at the item-category level or implicit intent modeling on existing knowledge graphs. However, such approaches struggle to capture grounded user intents and to handle sparsity and cold-start scenarios. In this work, we present LLM-based Intent Knowledge Graph Recommender (IKGR), a novel framework that constructs an intent-centric knowledge graph where both users and items are explicitly linked to intent nodes extracted by a tuning-free, RAG-guided LLM pipeline. By grounding intents in external knowledge sources and user profiles, IKGR canonically represents what a user seeks and what an item satisfies as first-class entities. To alleviate sparsity, we further introduce a mutual-intent connectivity densification strategy, which shortens semantic paths between users and long-tail items without requiring cross-graph fusion. Finally, a lightweight GNN layer is employed on top of the intent-enhanced graph to produce recommendation signals with low latency. Extensive experiments on public and enterprise datasets demonstrate that IKGR consistently outperforms strong baselines, particularly on cold-start and long-tail slices, while remaining efficient through a fully offline LLM pipeline.

翻译：基于大语言模型的推荐系统近期进展通常依赖于项目-类别层面的常识增强或现有知识图谱上的隐式意图建模。然而，此类方法难以捕捉具象化的用户意图，也难以处理稀疏性与冷启动场景。本研究提出基于大语言模型的意图知识图谱推荐系统（IKGR），该创新框架构建了一个以意图为中心的知识图谱，其中用户与项目均通过免调优、检索增强生成引导的大语言模型流程显式关联至提取的意图节点。通过将意图锚定于外部知识源与用户画像，IKGR以规范化方式将用户需求与项目满足的意图表征为一等实体。为缓解稀疏性问题，我们进一步提出互意图连接稠化策略，该策略无需跨图谱融合即可缩短用户与长尾项目间的语义路径。最后，在意图增强图谱上部署轻量级图神经网络层，以低延迟生成推荐信号。在公开及企业数据集上的大量实验表明，IKGR始终优于现有基线方法，尤其在冷启动与长尾数据片段上表现突出，同时通过全离线大语言模型流程保持高效运行。