Online Volume Optimization for Notifications via Long Short-Term Value Modeling

Yuchen Zhang, Mingjun Zhao, Chenglin Li, Weiyu Tou, Haolan Chen, Di Niu, Cunxiang Yin, Yancheng He, Fei Guo

Abstract: App push notifications are an essential tool for app developers to engage with their users actively and provide them with timely and relevant information about the app. However, determining the proper volume of notifications sent to each user is a key challenge for improving user experience, particularly for new users whose preferences on push notifications are unknown. In this paper, we address the problem of app notification volume optimization for newly onboarded users and propose a systematic approach to solve this problem. We incorporate a multi-task learning technique to accurately modeling both the short-term and long-term effects of different volumes of push notifications, and utilize online linear programming to achieve real-time notification allocation with volume constraints. We have conducted both offline and online experiments to evaluate the effectiveness of our method, and the results demonstrate that our approach dramatically improves multiple core metrics of the user experience, such as daily active users.