Hydra: A personalized and context-aware multi-modal transportation recommendation system

Hao Liu, Yongxin Tong, Panpan Zhang, Xinjiang Lu, Jianguo Duan, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Transportation recommendation is one important map service in navigation applications. Previous transportation recommendation solutions fail to deliver satisfactory user experience because their recommendations only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and largely overlook situational context. In this work, we propose Hydra, a recommendation system that offers multi-modal transportation planning and is adaptive to various situational context (e.g., nearby point-of-interest (POI) distribution and weather). We leverage the availability of existing routing engines and big urban data, and design a novel two-level framework that integrates uni-modal and multi-modal (e.g., taxi-bus, bus-cycle) routes as well as heterogeneous urban data for intelligent multi-modal transportation recommendation. In addition to urban context features constructed from multi-source urban data, we learn the latent representations of users, origin-destination (OD) pairs and transportation modes based on user implicit feedbacks, which captures the collaborative transportation mode preferences of users and OD pairs. A gradient boosting tree based model is then introduced to recommend the proper route among various uni-modal and multi-modal transportation routes. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy Hydra on Baidu Maps, one of the world's largest map services. Real-world urban-scale experiments demonstrate the effectiveness and efficiency of our proposed system. Since its deployment in August 2018, Hydra has answered over a hundred million route recommendation queries made by over ten million distinct users with 82.8% relative improvement of user click ratio.

Original languageEnglish (US)
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2314-2324
Number of pages11
ISBN (Electronic)9781450362016
DOIs
StatePublished - Jul 25 2019
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
CountryUnited States
CityAnchorage
Period8/4/198/8/19

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Keywords

  • Context-aware
  • Deployment
  • Feature engineering
  • Personalized
  • Transportation recommendation

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