用多模态网页表示让小模型比大模型更准地提取电商信息
Cross-Domain Web Information Extraction at Pinterest
- 融合结构、视觉和文本的紧凑网页表征，支持轻量模型学习
- 每秒处理超1000个网址，成本仅为最便宜GPT的千分之一
- 简单模型如XGBoost在属性提取上超越复杂LLM，适合工业级应用
互联网蕴含海量非结构化信息，但将其转化为结构化数据仍具挑战。在Pinterest，从电商网站精准提取结构化商品数据对提升用户体验和内容分发至关重要。本文提出Pinterest的属性提取系统，在可接受成本下实现高精度与高可扩展性。该方法采用一种新颖的网页表示，将结构、视觉与文本模态融合为紧凑形式，优化用于小型模型学习。此表示捕获每个可见HTML节点的文本、样式与布局信息。实验证明，此类表示使XGBoost等简单模型在属性提取上优于GPT等复杂大型语言模型。系统具备高度可扩展性，可每秒处理超过1000个URL，且成本仅为最便宜的GPT方案的千分之一。
原文摘要 · Abstract (English)
The internet offers a massive repository of unstructured information, but it's a significant challenge to convert this into a structured format. At Pinterest, the ability to accurately extract structured product data from e-commerce websites is essential to enhance user experiences and improve content distribution. In this paper, we present Pinterest's system for attribute extraction, which achieves remarkable accuracy and scalability at a manageable cost. Our approach leverages a novel webpage representation that combines structural, visual, and text modalities into a compact form, optimizing it for small model learning. This representation captures each visible HTML node with its text, style and layout information. We show how this allows simple models such as eXtreme Gradient Boosting (XGBoost) to extract attributes more accurately than much more complex Large Language Models (LLMs) such as Generative Pre-trained Transformer (GPT). Our results demonstrate a system that is highly scalable, processing over 1,000 URLs per second, while being 1000 times more cost-effective than the cheapest GPT alternatives.
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