动态构建知识图谱，解决冷启动推荐中数据少、更新难问题
Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation
- 从原始元数据动态构建可更新的知识图谱
- 无需预筛选候选集，在多跳推理中提升推荐准确率
- 适合新物品/用户快速上线的实时推荐场景
冷启动问题仍是真实推荐系统中的关键挑战，新物品因交互数据不足难以被有效推荐。尽管已有研究利用知识图谱（KG）和大语言模型（LLM）等外部知识，但静态知识图谱构建成本高且易过时，而基于LLM的方法受限于上下文窗口，依赖预筛选候选列表。为此，我们提出ColdRAG——一种检索增强框架：在推理时动态从原始元数据构建知识图谱，提取实体与关系形成可更新结构，并引入LLM引导的多跳推理，实现无需预筛选候选集的候选检索与排序。多基准测试结果表明，ColdRAG在多个数据集上持续优于7个强基线方法。
原文摘要 · Abstract (English)
The cold-start problem remains a critical challenge in real-world recommender systems, as new items with limited interaction data or insufficient information are frequently introduced. Despite recent advances leveraging external knowledge such as knowledge graphs (KGs) and large language models (LLMs), recommender systems still face challenges in practical environments. Static KGs are expensive to construct and quickly become outdated, while LLM-based methods depend on pre-filtered candidate lists due to limited context windows. To address these limitations, we propose ColdRAG, a retrieval-augmented framework that dynamically constructs a knowledge graph from raw metadata, extracts entities and relations to construct an updatable structure, and introduces LLM-guided multi-hop reasoning at inference time to retrieve and rank candidates without relying on pre-filtered lists. Experiments across multiple benchmarks show that ColdRAG consistently outperforms strong seven baselines.
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