Contextually Aware E-Commerce Product Question Answering using RAG
This addresses the challenge for e-commerce users in quickly finding accurate product information, though it appears incremental as it builds on existing RAG methods with contextual enhancements.
The paper tackled the problem of cognitive overload in e-commerce product pages by proposing a scalable, end-to-end framework for Product Question Answering using Retrieval Augmented Generation that integrates contextual understanding, resulting in a system that handles diverse queries and identifies information gaps.
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.