不用微调的LLM直接挖用户意图，让推荐系统在数据稀疏时也精准
Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intent
- 用无微调的RAG框架提取用户和物品的显式意图节点
- 在冷启动和长尾场景下效果优于主流基线，尤其提升稀疏数据表现
- 适合需要低延迟、高可解释性的工业级推荐系统
近期基于大语言模型（LLMs）的推荐方法多依赖物品类别层面的常识增强或现有知识图谱中的隐式意图建模，但难以捕捉真实用户意图，且在数据稀疏和冷启动场景下表现不佳。本文提出一种新型框架IKGR（基于意图的知识图谱推荐），通过无需微调的RAG引导式LLM流水线，从外部知识源与用户画像中提取显式意图节点，并将用户与物品直接关联至这些意图节点，使意图成为第一类实体。为缓解连接稀疏性，引入互意连通性增强策略，不依赖跨图融合即可缩短用户与长尾物品间的语义路径。最后，在增强后的意图图上使用轻量GNN层生成低延迟推荐信号。在公开及企业数据集上的大量实验表明，IKGR在冷启动和长尾样本上持续超越强基线，同时保持全离线推理效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。