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

arXiv:2606. 18803v1 Announce Type: new 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.