Near Real-time Optimization of Activity-based Notifications
| Autor: | Tao Zhongen, Shaunak Chatterjee, Changji Shi, Curtis Wang, Muralidharan Ajith, Jinyun Yan, PJ Xiao, Yan Gao, Romer Rosales, Yu Shipeng, Gupta Viral |
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| Rok vydání: | 2018 |
| Předmět: |
Event (computing)
Computer science 020204 information systems 0202 electrical engineering electronic engineering information engineering Mobile apps Volume (computing) 020201 artificial intelligence & image processing Social media 02 engineering and technology Computer security computer.software_genre computer |
| Zdroj: | KDD |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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