本文主要描述了百度地图中“智行”背后的算法实现。
主要是描述了算法背后的特征工程。
算法采用的是：gradient boosting tree
所以具体做法是：百度原来已经有了的uni-modal和multi-modal的查询结果做一个重新排序（智行）。
不过文章提到的 Since we train classiers sequentially, we approximate the gradient based on the previous step. 考虑了在线增量学习，给出了gradient boosting tree特定算法下的增量学习机制，值得学习。
另外有关特征描述部分，在考虑POI相关问题的时候，值得借鉴。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System | |||||||||||
| 文献作者 | Hao Liu; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Transportation recommendation; context-aware; personalized; feature engineering; deployment; POI 特征工程 | |||||||||||
| 摘要描述 | |||||||||||
| 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 oers multi-modal transportation planning and is adaptive to various situational contexts (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., taxibus, 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 eectiveness and eciency 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. | |||||||||||
