# Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support

Source: [Graph-Enhanced Retrieval-Augmented Question Answering for E-Commerce Customer Support](<https://www.sota2.com/research/paper/68fa5e3f1e209131ce9e0835>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T18:39:59.265Z

Authors: Piyushkumar Patel

Published: 2025-09-15T04:17:42.000Z

## Abstract

E-Commerce customer support requires quick and accurate answers grounded in product data and past support cases. This paper develops a novel retrieval-augmented generation (RAG) framework that uses knowledge graphs (KGs) to improve the relevance of the answer and the factual grounding. We examine recent advances in knowledge-augmented RAG and chatbots based on large language models (LLM) in customer support, including Microsoft's GraphRAG and hybrid retrieval architectures. We then propose a new answer synthesis algorithm that combines structured subgraphs from a domain-specific KG with text documents retrieved from support archives, producing more coherent and grounded responses. We detail the architecture and knowledge flow of our system, provide comprehensive experimental evaluation, and justify its design in real-time support settings. Our implementation demonstrates 23\\% improvement in factual accuracy and 89\\% user satisfaction in e-Commerce QA scenarios.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.14267>)

[PDF](<https://arxiv.org/pdf/2509.14267.pdf>)