Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to select pre-defined scenarios, which may hinder the downstream ranking performance. In addition, in the academic community, the multi-scenario ranking works only come from other fields, and there are no works specifically focusing on route data due to lacking a publicly available MSRR dataset. Moreover, all the existing multi-scenario works still fail to address the three specific challenges of MSRR simultaneously, i.e. explosion of scenario number, high entanglement, and high-capacity demand. Different from the prior, to address MSRR, our key idea is to factorize the complicated scenario in route ranking into several disentangled factor scenario patterns. Accordingly, we propose a novel method, Disentangled Scenario Factorization Network (DSFNet), which flexibly composes scenario-dependent parameters based on a high-capacity multi-factor-scenario-branch structure. Then, a novel regularization is proposed to induce the disentanglement of factor scenarios. Furthermore, two extra novel techniques, i.e. scenario-aware batch normalization and scenario-aware feature filtering, are developed to improve the network awareness of scenario representation. Additionally, to facilitate MSRR research in the academic community, we propose MSDR, the first large-scale publicly available annotated industrial Multi-Scenario Driving Route dataset. Comprehensive experimental results demonstrate the superiority of our DSFNet, which has been successfully deployed in AMap to serve the major online traffic.

翻译：多场景路线排序（MSRR）在众多工业地图系统中至关重要。然而，工业界主要采用交互式界面鼓励用户选择预设场景，这可能制约下游排序性能。此外，学术界现有的多场景排序工作仅来自其他领域，由于缺乏公开可用的MSRR数据集，尚无专门针对路线数据的研究。同时，现有所有多场景方法仍无法同时解决MSRR的三个特定挑战：场景数量爆炸、高度耦合和高容量需求。与既往研究不同，我们处理MSRR的核心思想是将路线排序中的复杂场景分解为多个解耦的因子场景模式。据此，我们提出了一种新方法——解耦场景因子分解网络（DSFNet），该方法基于高容量多因子场景分支结构灵活组合场景相关参数。随后，我们提出新型正则化方法诱导因子场景的解耦。此外，我们开发了场景感知批归一化和场景感知特征过滤两项附加新技术，以增强网络对场景表征的感知能力。为促进学术界的MSRR研究，我们提出了首个大规模公开可用的工业标注多场景驾驶路线数据集MSDR。综合实验结果表明，我们的DSFNet具有显著优越性，该模型已成功部署于高德地图服务主要在线交通场景。