  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.
