  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.

Machine Learning;Information Retrieval