Near Real-time Optimization of Activity-based Notifications

被引:9
|
作者
Gao, Yan [1 ]
Gupta, Viral [1 ]
Yan, Jinyun [1 ]
Shi, Changji [1 ]
Tao, Zhongen [1 ]
Xiao, P. J. [1 ]
Wang, Curtis [1 ]
Yu, Shipeng [1 ]
Rosales, Romer [1 ]
Muralidharan, Ajith [1 ]
Chatterjee, Shaunak [1 ]
机构
[1] LinkedIn Corp, Mountain View, CA 94043 USA
关键词
Notifications; Stream computing; Optimization; Machine learning;
D O I
10.1145/3219819.3219880
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, social media applications (e.g., Facebook, LinkedIn) have created mobile applications (apps) to give their members instant and real-time access from anywhere. To keep members informed and drive timely engagement, these mobile apps send event notifications. However, sending notifications for every possible event would result in too many notifications which would in turn annoy members and create a poor member experience. In this paper, we present our strategy of optimizing notifications to balance various utilities (e.g., engagement, send volume) by formulating the problem using constrained optimization. To guarantee freshness of notifications, we implement the solution in a stream computing system in which we make multi-channel send decisions in near real-time. Through online A/B test results, we show the effectiveness of our proposed approach on tens of millions of members.
引用
收藏
页码:283 / 292
页数:10
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