ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
Quick Answer
ProfiLLM enhances industrial ride-hailing dispatch by utilizing LLMs for user profiling, achieving up to 6.14% AUC improvement and 4.35% GMV gain in simulations.
Quick Take
Deployed on DiDi's platform, it addresses challenges of user data sparsity and context limitations through innovative profiling techniques.
Key Points
- ProfiLLM uses to create adaptive user profiles for ride-hailing dispatch.
- Achieved a 6.14% relative AUC improvement in outcome prediction.
- Generated a 4.35% GMV gain in dispatching simulations.
- Implemented on DiDi's production dispatcher with positive A/B test results.
- Addresses user data sparsity and context limitations in profiling.
Paper Resources
Source Excerpt
Bringing (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem. Production matching pipelines remain dominated by structured numerical features, yet decisive behavioral signals (e. g. , a driver's habitual aversion to certain regions) are inherently contextual and naturally expressible as LLM-generated user profiles. However, scaling such profiling to a live, mill
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