SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents
LLM-based web agents can navigate live storefronts, yet they often collapse to a single "average buyer" policy, failing to capture the heterogeneous and distributional nature of real buyer populations. Existing personalization methods rely on hand-crafted prompt-based personas that are brittle, difficult to scale, context-inefficient, and unable to faithfully represent population-level behavior. We introduce SimPersona, a novel framework that learns discrete buyer types from historical traffic a
Record details
Published: 14 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (14 May 2026), “SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents,” evidence record 4358, https://ethics.ai/record/4358 (originally published by arXiv).
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