TRACE: Agentic Catalog Enrichment with Multi-source Evidence Grounding

2026-08-21

AI Summary

本文提出TRACE框架，利用大型语言模型自动丰富电商产品目录属性，通过多源证据验证提高属性覆盖率和准确性。

Abstract

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%.

Problem

• e-commerce
• product catalog
• attribute-sparse
• search and recommendation

Innovation

• TRACE
• agentic Large Language Models (LLMs)
• ScoutAgent
• JudgeAgent
• automated catalog attribute enrichment