用大模型构建结构化属性图，提升电商实体搜索精度。
From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
- 用大模型从文本提取属性，构建分品类的结构化属性图
- 线上推理时基于结构图排序，降低57%令牌消耗，平均精度提升超5%
- 无需训练数据，跨品类通用，适合实际部署
实体搜索在电商场景中面临产品相似性随类别与上下文变化的挑战。传统基于嵌入的方法难以捕捉上下文相关的属性相关性。本文提出两阶段方法：离线阶段利用大模型从非结构化文本中提取结构化属性，构建具有品类感知模式的可复用属性图；在线阶段通过推理该结构化表示而非原始文本进行候选排序，将每产品令牌使用量减少57%，同时提升排名精度。实验表明，该方法在零样本场景下优于多个基线，平均精度提升超过5%，且在多种产品类别间具有良好泛化能力，具备实际部署潜力。
原文摘要 · Abstract (English)
Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Traditional embedding-based approaches often struggle to capture nuanced context-specific attribute relevance. In this paper, we present a two-stage approach combining Large Language Model (LLM)-driven attribute graph construction with graph-aware LLM ranking. In the offline stage, we extract structured product attributes from unstructured text, and construct a reusable attribute graph with category-aware schemas. In the online stage, we rank retrieved candidates by reasoning over this structured representation rather than raw text, reducing per-product token usage by 57% while improving ranking precision. Experiments show that our approach outperforms multiple baselines under zero-shot scenarios, achieving a over 5% improvement in average precision without requiring training data, generalizes robustly across diverse product categories, and shows immense potential for real-world deployment.
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