@article{Xu2026, 
author = {Changliang Xu and Yukun Kang and Quan Feng and Jinghua Hua and Piji Li and Feiran Wu and Hu Wei and Xiang Chen and Sheng-Jun Huang and Songcan Chen},
title = {ItemRAG: Retrieval-Augmented Generation with Item-Based Knowledge Computing for E-Commerce Product Question Answering},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {2},
pages = {407-424},
keywords = {Large Language Models (LLMs), retrieval augmentation, knowledge computing, e-commerce customer service},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020080},
doi = {10.26599/BDMA.2025.9020080},
abstract = {The integration of Large Language Models (LLMs) into e-commerce platforms has significantly enhanced user experience through personalized recommendations and automated customer support. However, existing Retrieval-Augmented Generation (RAG) frameworks face challenges when applied to e-commerce product Question Answering (QA), such as handling extensive product catalogs, ensuring timely knowledge updates, and maintaining efficient retrieval performance. In this paper, we propose ItemRAG, a novel framework that combines RAG with item-based knowledge computing to address these challenges. ItemRAG decouples QA templates from specific products by leveraging a dynamic knowledge graph, enabling efficient updates and reducing the size of the knowledge base. The framework includes state analysis to capture user intent and context, grouped indexing for efficient retrieval, and knowledge computing to dynamically generate accurate answers. Experimental results demonstrate that decoupled-based ItemRAG significantly outperforms the Coupled-based RAG approaches (CoupledRAG) in retrieval accuracy and generation quality, achieving higher precision, recall, F1-score, and factual correctness. Our work highlights the efficacy of integrating the knowledge graph with RAG to enhance LLM-based e-commerce customer service systems.}
}