DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
<|reference_start|>DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking: 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.<|reference_end|>
@article{yu2024dsfnet:,
    title={DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario
  Route Ranking},
    author={Jiahao Yu, Yihai Duan, Longfei Xu, Chao Chen, Shuliang Liu, Kaikui
  Liu, Fan Yang, Xiangxiang Chu, Ning Guo},
    journal={arXiv preprint arXiv:2404.00243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00243},
    primaryClass={cs.IR}
}