Grounding AI shopping agents using personas learned from raw clickstream data
A new framework called SimPersona has been introduced to enhance e-commerce agents by learning discrete buyer personas from raw clickstream data. This approach aims to overcome the limitations of existing personalization methods that rely on average buyer profiles. SimPersona has shown promising results, achieving a high conversion-rate alignment with real buyers while maintaining the diversity of buyer behavior.
- ▪SimPersona learns discrete buyer types from historical traffic data and uses them as persona tokens for LLM-based web agents.
- ▪The framework captures the statistical structure of real buyer behavior and merchant-specific buyer populations.
- ▪SimPersona has been evaluated on 8.37 million buyers across 42 live storefronts, achieving 78% conversion-rate alignment with actual buyers.
Opening excerpt (first ~120 words) tap to expand
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 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 View PDF HTML (experimental) 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
Discussion
0 commentsMore from arXiv.org
