# Contextually Aware E-Commerce Product Question Answering using RAG

arXiv: https://arxiv.org/abs/2508.01990 · HTML: https://mlsanity.com/paper/2508.01990/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://mlsanity.com/paper/2305.15294/) (2023)
- [AmazonQA: A Review-Based Question Answering Task](https://mlsanity.com/paper/1908.04364/) (2019)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [Reinforcement Learning for Optimizing RAG for Domain Chatbots](https://mlsanity.com/paper/2401.06800/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Modular LLM Framework for Explainable Price Outlier Detection](https://mlsanity.com/paper/2603.20636/) (2026)
- [Ottilie: A Socially Intelligent Virtual Host for Sales-Driven Live Commerce](https://mlsanity.com/paper/2605.14542/) (2026)

## Abstract (arXiv)

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.