Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

TL;DR

This paper introduces an Entity Graph Learning system for user targeting that addresses cold-start issues and offers explainability, combining offline entity graph construction with real-time online targeting for scalable and transparent marketing.

Contribution

The paper proposes a novel EGL system with a Three-stage Relation Mining Procedure that eliminates the need for seed users and enhances explainability in user targeting.

Findings

Superior offline performance demonstrated in experiments

Effective real-time targeting in online A/B tests

Addresses cold-start and transparency issues in user targeting

Abstract

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.