ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

2026-06-17 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Advanced, medium

Summary

ProfiLLM is an agentic Large Language Model (LLM) data pipeline designed for utility-aligned user profiling in industrial ride-hailing dispatch systems. It addresses challenges of platform-scale behavioral logs, long-tail users, and ensuring profiles enhance prediction utility. The system comprises two modules: Tool-Augmented Global Knowledge Mining, which uses 27 analytical tools to extract global knowledge, user clustering rules, and supply-demand priors; and Utility-Aligned Profile Exploration, which generates, evaluates, and refines candidate profiles for DPO fine-tuning. Deployed on DiDi's production dispatcher, ProfiLLM achieved up to +6.14% relative AUC improvement in outcome prediction and +4.35% GMV gain in dispatching simulation. A 14-day online A/B test further showed +0.47% GMV, +0.33% Completion Rate, and -0.82% Cancel-Before-Accept rate.

Key takeaway

For AI Engineers optimizing industrial dispatch systems, ProfiLLM demonstrates a viable path to integrate LLM-generated user profiles at scale. You should consider agentic LLM pipelines with tool augmentation to overcome context window limitations and long-tail user challenges. Prioritize utility-aligned profile generation and refinement using lightweight proxies to ensure direct impact on key metrics like GMV and completion rates. This approach offers significant performance gains in real-world production environments.

Key insights

Operationalizing utility-aligned LLM-generated user profiles for large-scale ride-hailing dispatch using agentic data pipelines.

Principles

• LLMs can extract decisive behavioral signals from platform logs.
• Utility alignment is crucial for LLM profiles in prediction systems.
• Agentic LLMs with tools can manage platform-scale data constraints.

Method

ProfiLLM uses tool-augmented global knowledge mining for clustering and priors, then utility-aligned exploration to generate and refine profiles via a lightweight proxy and DPO fine-tuning.

In practice

• Integrate LLM-generated profiles into production matching pipelines.
• Use lightweight utility proxies for iterative profile refinement.
• Employ DPO fine-tuning with preference pairs for profile optimization.

Topics

• Ride-Hailing Dispatch
• User Profiling
• Large Language Models
• Agentic AI
• DPO Fine-tuning
• Data Pipelines

Code references

• SwordFaith/RAVine

Best for: AI Architect, NLP Engineer, AI Scientist, AI Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.