Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System
Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System
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DOI：
10.1145/3292500.3330660
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发表时间：
2019-07
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影响因子：
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通讯作者：
Hao Liu;Yongxin Tong;Panpan Zhang;Xinjiang Lu;Jianguo Duan;Hui Xiong
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作者：
Hao Liu;Yongxin Tong;Panpan Zhang;Xinjiang Lu;Jianguo Duan;Hui Xiong
交通推荐是导航应用中一项重要的地图服务。以往的交通推荐方案无法提供令人满意的用户体验，因为它们的推荐只考虑了一种交通模式（单式交通模式，如出租车、公交车、自行车）的路线，并且很大程度上忽略了情境。在这项工作中，我们提出了Hydra，这是一个提供多式联运规划的推荐系统，可适应各种情景背景（例如，附近的兴趣点（POI）分布和天气）。我们利用现有路线引擎和大城市数据的可用性，设计了一个新的两级框架，该框架集成了单式和多式（例如，出租车-公共汽车，公共汽车-自行车）路线以及异构城市数据，用于智能多式联运推荐。基于多源城市数据构建城市文脉特征，基于用户隐式反馈学习用户、始发目的地（OD）对和交通方式的潜在表征，捕捉用户和OD对协同交通方式的偏好。然后引入基于梯度提升树的模型，在各种单式和多式联运路线中推荐合适的路线。我们还对框架进行了优化，以支持实时、大规模的路由查询和推荐。我们将Hydra部署在百度地图上，百度地图是世界上最大的地图服务之一。实际城市规模的实验证明了我们提出的系统的有效性和效率。自2018年8月部署以来，Hydra已经回答了超过1000万独立用户的1亿多条路线推荐查询，用户点击率相对提高了82.8%。
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.