Abstract
Push Notification (PN) and Personalized Push Notifications (PPN) are key contemporary topics in mobile app industry today. Push notifications provide a viable content recommendation channel which complements in-app recommendation in mobile apps. There are existing algorithms for in-app content recommendation, however, the PN based recommendation systems are still under research. In this paper, we present Clairvoyant-Push — a novel Personalized Push Notification system based on user segmentation and social scoring. User segmentation is done by using the Latent Dirichlet Allocation (LDA) based topic modeling. Moreover, social scoring is used to assign score to each articles to filter out the quality news content for each segments. We have deployed and tested our proposed system using A/B testing framework. The results show an average of 89% lift in opening rate compared to the control group. Further, the results indicate that our system is outperforming with an opening rate of 1012% compared to the industry standard personalised push opening rate of 6–8%.
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