Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation

TL;DR

This paper introduces PGHS, a dual-process simulation framework that combines decision policies and large language models to accurately simulate merchant user behavior for strategy evaluation.

Contribution

The paper proposes a novel hybrid simulation method that integrates policy learning and LLM reasoning to address structural challenges in user behavior modeling.

Findings

PGHS achieves a group simulation error of 8.80%.

It improves over baseline methods by 45.8% and 40.9%.

Deployed on Meituan with 101 merchants and 26,000 trajectories.

Abstract

Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, building a trustworthy simulator faces two structural challenges. First, information incompleteness causes reasoning-based simulators to over-rationalize when unobserved factors such as offline context and implicit habits are missing. Second, mechanism duality requires capturing both interpretable preferences and implicit statistical regularities, which no single paradigm achieves alone. We propose Policy-Guided Hybrid Simulation (PGHS), a dual-process framework that mines transferable decision policies from behavioral trajectories and uses them as a shared alignment layer. This layer anchors an LLM-based reasoning branch that prevents over-rationalization and an ML-based fitting branch that absorbs implicit regularities. Group-level predictions from both branches are fused for complementary correction. We deploy PGHS on Meituan with 101 merchants and over 26,000 trajectories. PGHS achieves a group simulation error of 8.80%, improving over the best reasoning-based and fitting-based baselines by 45.8% and 40.9% respectively.