DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要