Learning from Online User Feedback for Shopping Agents

Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable supervision for improving these agents. However, existing approaches primarily rely on offline training signals, such as user-item interactions or synthetic preference data, while largely overlooking the rich supervision contained in users' natural conversational feedback. Moreover, the available online feedback is hete

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ethics.ai (12 August 2026), “Learning from Online User Feedback for Shopping Agents,” evidence record 18799, https://ethics.ai/record/18799 (originally published by arXiv).

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