CoMiner: nationwide behavior-driven unsupervised spatial coordinate mining from uncertain delivery events
CoMiner: nationwide behavior-driven unsupervised spatial coordinate mining from uncertain delivery events
复制标题
DOI：
10.1145/3557915.3560944
复制
发表时间：
2022-11
期刊：
影响因子：
--
通讯作者：
Zhiqing Hong;Guang Wang;Wenjun Lyu;Baoshen Guo;Yi Ding;Haotian Wang;Shuai Wang;Yunhuai Liu;Desheng Zhang
中科院分区：
文献类型：
--
作者：
Zhiqing Hong;Guang Wang;Wenjun Lyu;Baoshen Guo;Yi Ding;Haotian Wang;Shuai Wang;Yunhuai Liu;Desheng Zhang
地理编码，将文本地址与相应的 GPS 坐标关联起来，对于许多基于位置的服务（例如物流、拼车和社交网络）至关重要。最常见的地理编码解决方案之一是使用商业地图服务（例如 Google 地图），通过上传文本地址来获取相应的坐标。然而，由于商业竞争和高成本（经常性费用）等现实挑战，这对于某些基于位置的服务提供商来说通常不切实际。在本文中，我们设计了一种新的经济高效的地理编码框架，可以自动从服务提供商的文本地址推断地理坐标。为了实现这一目标，我们以电商物流服务为具体场景，设计了 CoMiner，一个基于文本地址数据、投递事件数据和快递轨迹数据的无监督坐标推理框架。 CoMiner 中有三个主要组件。 （1）通过对不同空间粒度下顾客的购物模式进行建模来建立POI级聚类模型； (2) 通过对快递员的递送事件和地理坐标进行建模来构建递送移动性图（DMG）； (3)行为驱动的地址排序模型，通过挖掘快递员的不确定报告行为来进一步推断DMG上的坐标。我们通过从数据驱动实验到实际部署的三阶段评估来广泛验证 CoMiner 的性能。 (i) 我们对三个大型数据集进行了广泛的实验，其中 CoMiner 的平均准确率达到 95.1%，比最先进的方法高出 20.3%。 (ii) 我们在京东物流部署了 CoMiner，推断了超过 3000 万个地址的坐标，平均准确率为 93.3%。 (iii) 我们将 CoMiner 用于两个基于地理编码的应用程序，即包裹重新路由优化和异常交付事件检测。
Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services (e.g., Google Maps) by uploading textual addresses to obtain corresponding coordinates. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses for service providers. To achieve this, we take the E-Commerce logistics service as a concrete scenario and design CoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data. There are three main components in CoMiner. (1) A POI-level clustering model by modeling customers' shopping patterns at different spatial granularities; (2) A Delivery Mobility Graph (DMG) by modeling couriers' delivery events and geographic coordinates; (3) A behavior-driven address ranking model by mining couriers' uncertain reporting behaviors to further infer coordinates on DMG. We extensively verify the performance of CoMiner with a three-phase evaluation from data-driven experiments to real-world deployment. (i) We conduct extensive experiments on three large-scale datasets where CoMiner achieves an average accuracy of 95.1%, which outperforms the state-of-the-art methods by 20.3%. (ii) We deploy CoMiner in JD Logistics, inferring coordinates for over 30 million addresses with an average accuracy of 93.3%. (iii) We utilize CoMiner for two Geocoding-based applications, i.e., parcel re-routing optimization and abnormal delivery event detection.