用检索增强生成技术，让电商问答更懂用户上下文。
Contextually Aware E-Commerce Product Question Answering using RAG
- 基于RAG框架融合对话历史与用户画像，实现个性化回答
- 支持客观、主观及多意图问题，覆盖多种产品信息源
- 提出新评估指标，适用于各类RAG系统性能评测
电商平台产品页包含结构化参数、非结构化评论以及个性化优惠或地区差异等上下文信息。尽管信息丰富，但内容繁杂易造成认知过载，使用户难以快速准确获取所需信息。现有商品问答系统常无法有效利用用户上下文和多样化产品信息。本文提出一种可扩展的端到端电商商品问答框架，采用检索增强生成（RAG）技术深度融合上下文理解。系统结合对话历史、用户画像和商品属性，提供相关且个性化的回答，能有效处理客观、主观及多意图查询，覆盖异构信息源，并识别商品目录中的信息缺口，以支持持续内容优化。我们还引入新型评估指标，广泛适用于RAG系统性能评价。
原文摘要 · Abstract (English)
E-commerce product pages contain a mix of structured specifications, unstructured reviews, and contextual elements like personalized offers or regional variants. Although informative, this volume can lead to cognitive overload, making it difficult for users to quickly and accurately find the information they need. Existing Product Question Answering (PQA) systems often fail to utilize rich user context and diverse product information effectively. We propose a scalable, end-to-end framework for e-commerce PQA using Retrieval Augmented Generation (RAG) that deeply integrates contextual understanding. Our system leverages conversational history, user profiles, and product attributes to deliver relevant and personalized answers. It adeptly handles objective, subjective, and multi-intent queries across heterogeneous sources, while also identifying information gaps in the catalog to support ongoing content improvement. We also introduce novel metrics to measure the framework's performance which are broadly applicable for RAG system evaluations.
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