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arXiv CS.AI·27d ago·5 min read
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
TL;DR
Retail businesses face challenges in understanding customer behavior. A Large Behavior Model was developed as a promptable digital twin to simulate and predict retail customer actions.
✦ Why It Matters
Retailers can implement the Large Behavior Model today to enhance customer targeting and improve marketing ROI.
Key Takeaways
- Utilize LBM for accurate customer behavior predictions.
- Incorporate historical transaction data for better marketing strategies.
- Apply retrieval-augmented generation to enhance product context.
- Leverage reinforcement learning for evidence-based decision-making.
How It Works
LBM learns customer decision-making by analyzing large-scale retail transactions, creating behavioral profiles that reflect past purchases. It integrates product context through retrieval-augmented generation, allowing the model to generate more relevant recommendations.
The training process involves continued pre-training on behavioral data, followed by supervised fine-tuning and reinforcement learning, which ensures that the model relies on explicit behavioral evidence rather than generic language patterns.