[2605.14205] SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents
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Computer Science > Artificial Intelligence
arXiv:2605.14205 (cs)
[Submitted on 14 May 2026]
Title:SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents
Authors:Zahra Zanjani Foumani, Alberto Castelo, Shuang Xie, Ted Chaiwachirasak, Han Li, Lingyun Wang<br>View a PDF of the paper titled SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents, by Zahra Zanjani Foumani and 5 other authors
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Abstract: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 and exposes them to LLM-based web agents as compact persona tokens. Given raw clickstreams, a behavior-aware VQ-VAE induces a discrete buyer-type space that captures the statistical structure of real buyer behavior and merchant-specific buyer population distributions. To provide behavior-specific guidance to LLM-based web agents, SimPersona maps each learned buyer type to a dedicated persona token in the LLM agent vocabulary and fine-tunes the agent with these tokens on real browsing traces. At inference, each synthetic buyer is assigned to a learned buyer type with a single encoder forward pass, requiring no retraining or store-specific prompt engineering. For population-level simulation, SimPersona samples buyer types from each merchant's empirical distribution over the learned VQ-VAE codebook and instantiates agents with the corresponding persona tokens, preserving merchant-specific buyer population distributions. Evaluated on $8.37$M buyers across $42$ held-out live storefronts, SimPersona achieves $78\%$ conversion-rate alignment with real buyers, exhibits interpretable behavioral variation across buyer types, and outperforms a baseline with $8\times$ more parameters on goal-oriented shopping tasks. We further release an open-source data pipeline that converts raw e-commerce event logs into buyer representations and agent-training traces.
Subjects:
Artificial Intelligence (cs.AI)
Cite as:<br>arXiv:2605.14205 [cs.AI]
(or<br>arXiv:2605.14205v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.14205
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arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Zahra Zanjani Foumani [view email]<br>[v1]<br>Thu, 14 May 2026 00:01:11 UTC (9,594 KB)
Full-text links:<br>Access Paper:
View a PDF of the paper titled SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-Commerce Agents, by Zahra Zanjani Foumani and 5 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source
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