# GenMatch: An End-to-End Generative Matching Framework for Micro-View Order-Dispatching in Ride-Hailing

arXiv: https://arxiv.org/abs/2608.19751 · HTML: https://mlsanity.com/paper/2608.19751/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 18 of 18)

- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration](https://mlsanity.com/paper/2406.14017/) (2024)
- [MTGR: Industrial-Scale Generative Recommendation Framework in Meituan](https://mlsanity.com/paper/2505.18654/) (2025)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms](https://mlsanity.com/paper/1905.11353/) (2019)
- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms](https://mlsanity.com/paper/2105.08791/) (2021)
- [OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion](https://mlsanity.com/paper/2506.06913/) (2025)
- [Generative Next POI Recommendation with Semantic ID](https://mlsanity.com/paper/2506.01375/) (2025)
- [Optimizing Generative Ranking Relevance via Reinforcement Learning in Xiaohongshu Search](https://mlsanity.com/paper/2512.00968/) (2025)
- [From Clicks to Preference: A Multi-stage Alignment Framework for Generative Query Suggestion in Conversational System](https://mlsanity.com/paper/2508.15811/) (2025)
- [An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2408.10479/) (2024)
- [Generative Recommendation for Large-Scale Advertising](https://mlsanity.com/paper/2602.22732/) (2026)
- [APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization](https://mlsanity.com/paper/2603.02730/) (2026)
- [Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments](https://mlsanity.com/paper/2406.14635/) (2024)
- [MBGR: Multi-Business Prediction for Generative Recommendation at Meituan](https://mlsanity.com/paper/2604.02684/) (2026)
- [UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale](https://mlsanity.com/paper/2606.00422/) (2026)

## Abstract (arXiv)

Micro-View Order-Dispatching assigns available drivers to passenger orders within each dispatch batch and is critical to the service quality and operational efficiency of ride-hailing platforms. Mainstream industrial solutions follow a multi-stage paradigm of model prediction, value calculation, and dispatch matching. Although dispatch quality is determined by the final batch-level assignment, these stages optimize different intermediate objectives. This cross-stage objective inconsistency means that improving a single stage does not necessarily improve the overall dispatch result. We therefore formulate Micro-View Order-Dispatching as a generative matching problem and propose GenMatch, an end-to-end Generative Matching framework and the first such framework deployed in a real-world production environment. Applying generative modeling to this problem introduces three challenges. First, each dispatch batch forms a dynamic sparse bipartite graph, requiring efficient structured batch-level encoding. Second, replacing the hand-crafted value function requires learning unified business utility from heterogeneous feedback. Third, directly generating an assignment requires tracking the evolving matching state because each selected order-driver pair changes the remaining feasible candidates. GenMatch addresses these challenges with a Context-Aware Bipartite Encoder, a Business-Aware Utility Learner, and a State-Aware Pointer Decoder. Extensive offline evaluations and online A/B tests in five cities across DiDi's international ride-hailing markets show consistent improvements over competitive baselines, confirming the effectiveness and practicality of GenMatch for industrial order-dispatching.