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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 multi-task deep learning based 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. Moreover, we propose two models to recommend the proper route among various uni-modal and multi-modal transportation routes: (1) a light-weight gradient boosting decision tree (GBDT) based recommendation model; and (2) a multi-task wide and deep learning (MTWDL) based recommendation model. We also optimize the framework to support real-time, large-scale route query and recommendation. We deploy Hydra on Baidu Maps, 1 1. https: //maps. baidu. com/. 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. The GBDT based model and MTWDL based model achieve 82. 8 and 96. 6 percent relative improvement of user click ratio, respectively.
Liu et al. (Fri,) studied this question.