用智能体自动补全电商目录缺失属性，提升推荐效果。
TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding
- 用多源证据三角验证，智能体自动挖掘商品属性
- 离线评估准确率98.2%，覆盖率达74.7%；上线后覆盖提升90.4%
- 适合做电商推荐系统优化和自动化数据治理的团队
电商平台的商品目录支撑搜索、发现与推荐，但常存在属性缺失：消费者和下游系统依赖的属性要么藏在标题、图片等非结构化内容中，要么完全缺失。人工补全如此大规模且快速增长的目录不现实。本文提出TRACE框架，利用代理型大语言模型实现自动化目录属性增强。ScoutAgent在商家目录、聚合数据源和身份匹配的网页搜索中交叉验证多模态证据，提出候选属性值及佐证；JudgeAgent则评估每个属性值与其证据的一致性，决定是否发布或转交人工审核。在离线人类评估数据集上，TRACE提出的属性值准确率达98.2%，属性覆盖率为74.7%。在工业级目录生产环境中部署后，四个业务垂直领域的印象加权增强覆盖率提升了90.4%。在线实验表明，在商品详情页展示增强后的属性，使结账转化率提升了0.48%。
原文摘要 · Abstract (English)
Product catalogs underpin search, discovery, and recommendation in e-commerce, yet they are often attribute-sparse: the attributes shoppers and downstream systems rely on are either buried in unstructured content such as titles and images or missing from the catalog altogether. Manually enriching e-commerce catalogs is impractical given their scale and rapid growth. This paper introduces TRACE, a novel framework for automated catalog attribute enrichment using agentic Large Language Models (LLMs). A ScoutAgent triangulates multimodal evidence across merchant catalogs, syndicated feeds, and identity-matched web search to propose candidate attribute values with supporting evidence, while a JudgeAgent verifies the proposed value for each attribute value against its supporting evidence and decides whether to publish it or route it to human review. On an offline human evaluation dataset, TRACE's proposed attribute values were 98.2% accurate at 74.7% attribute coverage. Deployed in production on an industry-scale catalog, TRACE increased impression-weighted enrichment coverage across four business verticals by 90.4%. An online experiment subsequently showed that surfacing the enriched attributes on the product detail page increased checkout conversion by 0.48%.
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