ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
ProfiLLM is an agentic LLM data pipeline that creates utility-aligned user profiles for ride-hailing dispatch. It uses tool-augmented global knowledge mining and utility-aligned profile exploration to overcome scaling challenges. Deployed on DiDi, it achieves significant improvements in prediction AUC, GMV, and completion rates.
[2606.18803] ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
[Submitted on 17 Jun 2026]
Title:ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch
View a PDF of the paper titled ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch, by Tengfei Lyu and 6 other authors
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Abstract:Bringing Large Language Models (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, millisecond-latency dispatcher faces three intertwined constraints rarely addressed together: on a platform with millions of daily orders, logs exceed any LLM's context window by orders of magnitude; most users are long-tail, with too few interactions for per-user profiling; and surface-fluent profiles do not necessarily improve downstream prediction utility. We present ProfiLLM, an agentic LLM data pipeline that operationalizes utility-aligned user profiling for production matching systems through two modules. (1) Tool-Augmented Global Knowledge Mining equips an LLM agent with 27 analytical tools to mine platform-scale data, producing reusable global knowledge, adaptive user clustering rules, and region-level supply-demand priors. (2) Utility-Aligned Profile Exploration generates multiple candidate profiles per cluster, evaluates them via a lightweight downstream utility proxy, iteratively refines the best candidates and constructs preference pairs for DPO fine-tuning. Deployed on DiDi's production dispatcher, ProfiLLM achieves up to +6.14% relative AUC improvement in outcome prediction, up to +4.35% GMV gain in dispatching simulation, and consistent improvements in a 14-day online A/B test including +0.47% GMV, +0.33% Completion Rate, and -0.82% Cancel-Before-Accept rate.
Subjects:
Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2606.18803 [cs.AI]
(or arXiv:2606.18803v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.18803
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tengfei Lyu [view email] [v1] Wed, 17 Jun 2026 08:15:07 UTC (11,200 KB)
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