mlsanityData to 14 Oct 2026

Contextually Aware E-Commerce Product Question Answering using RAG

arXiv 2508.01990 ↗First posted 4 Aug 2025cs.CLcited by 2

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

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.

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.

Builds on

7 of 24 references.

References · 7

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  3. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  4. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 346
  5. 2019AmazonQA: A Review-Based Question Answering Taskcited by 43
  6. 2019Product-Aware Answer Generation in E-Commerce Question-Answeringcited by 27
  7. 2024Reinforcement Learning for Optimizing RAG for Domain Chatbotscited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026A Modular LLM Framework for Explainable Price Outlier Detectioncited by 0
  2. 2026Ottilie: A Socially Intelligent Virtual Host for Sales-Driven Live Commercecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support
  2. 2024RAG based Question-Answering for Contextual Response Prediction System
  3. 2024KaPQA: Knowledge-Augmented Product Question-Answering
  4. 2025QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering
  5. 2026Augmenting Question Answering with A Hybrid RAG Approach
  6. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
  7. 2025From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data
  8. 2024Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model ↗