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arXiv · 2609.38974

RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce

Xinwei Yang·Kelong Mao·Yudong Guo·Sulong Xu·Simiu Gu·Chen Huang·Wenqiang Lei
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Abstract

Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users reveal and revise constraints, coordinate multiple goals, and expect product-grounded recommendations over a full conversation. Existing benchmarks are mostly outcome-oriented or execution-oriented, leaving this evolving decision process under-evaluated. We introduce REALWORLDSHOP, a benchmark built on 3.28M grounded products, structured shopping episodes, a profile-grounded and actioncontrolled user simulator, and role-play evaluation. Our analysis shows that current systems produce locally plausible responses but struggle with state tracking, constraint updating, and grounded convergence, especially under ambiguous intent, bundle, and multi-intent scenarios. We further propose REALSHOP_AGENT, an executable session-control framework with explicit state management, shopping-flow control, catalog-grounded retrieval, and runtime guards. Experiments show that REALSHOP_AGENT consistently outperforms strong baselines on REALWORLDSHOP.

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Xinwei Yang, Kelong Mao, Yudong Guo, Sulong Xu, Simiu Gu, Chen Huang, Wenqiang Lei. 2026-09-30. RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce. https://arxiv.org/abs/2609.38974

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