DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking

• URL: http://arxiv.org/abs/2404.00243v2
• Date: Tue, 05 Nov 2024 11:46:14 GMT
• Title: DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
• Authors: Jiahao Yu, Yihai Duan, Longfei Xu, Chao Chen, Shuliang Liu, Kaikui Liu, Fan Yang, Xiangxiang Chu, Ning Guo,
• Abstract summary: Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. We propose a novel method, Disentangled Scenario Factorization Network (DSFNet), which flexibly composes scenario-dependent parameters. We also propose MSDR, the first large-scale publicly available annotated industrial Multi-Scenario Driving Route dataset.
• Score: 19.448901545665212
• License:
• Abstract: 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.

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