With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users that are interested in specific services. Most pioneering works for user targeting tasks commonly perform similarity-based expansion with a few active users as seeds, suffering from the following major issues: the unavailability of seed users for newcoming services and the unfriendliness of black-box procedures towards marketers. In this paper, we design an Entity Graph Learning (EGL) system to provide explainable user targeting ability meanwhile applicable to addressing the cold-start issue. EGL System follows the hybrid online-offline architecture to satisfy the requirements of scalability and timeliness. Specifically, in the offline stage, the system focuses on the heavyweight entity graph construction and user entity preference learning, in which we propose a Three-stage Relation Mining Procedure (TRMP), breaking loose from the expensive seed users. At the online stage, the system offers the ability of user targeting in real-time based on the entity graph from the offline stage. Since the user targeting process is based on graph reasoning, the whole process is transparent and operation-friendly to marketers. Finally, extensive offline experiments and online A/B testing demonstrate the superior performance of the proposed EGL System.

翻译：随着各类移动设备的普及，用户靶向（定位对特定服务感兴趣的目标用户）越来越受到关注，其目标在于高效、精准地锁定目标用户。现有用户靶向任务的主流方法通常以少量活跃用户为种子进行基于相似性的扩展，但存在两大核心问题：新服务缺乏种子用户，以及黑盒过程对营销人员不友好。针对上述问题，本文设计了一个实体图学习（EGL）系统，在提供可解释用户靶向能力的同时，可有效应对冷启动问题。EGL系统采用混合离-在线架构，以满足可扩展性和及时性需求。具体而言，在离线阶段，系统专注于重型实体图构建与用户实体偏好学习，其中我们提出三阶段关系挖掘流程（TRMP），摆脱了对昂贵种子用户的依赖。在线阶段，系统基于离线阶段构建的实体图实现实时用户靶向。由于用户靶向过程基于图推理，整个流程透明且对营销人员操作友好。最终，大量离线实验与在线A/B测试证明了所提出的EGL系统的优越性能。