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KDD 2018 | Near real-time optimization of activity-based notifications

Accepted Papers

Near real-time optimization of activity-based notifications

Yan Gao (LinkedIn); Viral Gupta (LinkedIn); Jinyun Yan (LinkedIn); Changji Shi (LinkedIn); Zhongen Tao (LinkedIn); Pj Xiao (LinkedIn); Curtis Wang (LinkedIn); Shipeng Yu (LinkedIn); Romer Rosales (LinkedIn); Ajith Muralidharan (LinkedIn); Shaunak Chatterjee (LinkedIn)

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.

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