发表机构
DoorDash（多闸道科技（DoorDash））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电商商品目录属性稀疏问题，提出基于LLM的TRACE框架，经离线评估、生产部署及在线实验验证，可高效提升目录属性丰富的准确率、覆盖率及结账转化率。
AI 中文摘要
商品目录是电子商务中搜索、发现和推荐的基础，但通常存在属性稀疏的问题：购物者和下游系统依赖的属性要么埋藏在标题、图像等非结构化内容中，要么完全缺失于目录。考虑到电子商务目录的规模和快速增长，手动丰富目录是不切实际的。本文提出TRACE，一种利用智能体式大语言模型（LLM）实现自动化目录属性丰富的新型框架。ScoutAgent（侦察智能体）整合商家目录、联合数据源和身份匹配的网络搜索中的多模态证据，提出候选属性值及支撑证据；JudgeAgent（裁决智能体）对照支撑证据验证每个属性值，决定是否发布该值或提交人工审核。在离线人工评估数据集上，TRACE提出的属性值准确率达98.2%，属性覆盖率为74.7%；在工业规模目录的生产环境部署后，TRACE使四个业务垂直领域的曝光加权丰富覆盖率提升90.4%；后续在线实验显示，在商品详情页展示丰富后的属性使结账转化率提升0.48%。
英文摘要
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%.
Comments12 pages, 2 figures
