Large Behavior Model: A Promptable Digital Twin of the Retail Customer
Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data. We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Customer state is represented by a behavioral profile derived from historical purchases, while product context is incorporated through retrieval-augmented generation. The model is trained using continued pre-training on verbalized behavioral data, supervised fine-tuning for deci...
| Authors | Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn |
|---|---|
| Primary category | cs.AI |
| Published | 8 July 2026 |
| Last updated at source | 10 July 2026 |
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