RichNote: Adaptive Selection and Delivery of Rich Media Notifications to Mobile Users

In recent years, notification services for social networks, mobile apps, messaging systems and other electronic services have become truly ubiquitous. When a new content becomes available, the service sends an instant notification to the user. When the content is produced in massive quantities, and it includes both large-size media and a lot of meta-information, it gives rise to a major challenge of selecting content to notify about and information to include in such notifications. We tackle three important challenges in realizing rich notification delivery: (1) content and presentation utility modeling, (2) notification selection and (3) scheduling of delivery. We consider a number of progressive presentation levels for the content. Since utility is subjective and hard to model, we rely on real data and user surveys. We model the content utility by learning from large-scale real world data collected from Spotify music streaming service. For the utility of the presentation levels we rely on user surveys. Blending these two techniques together, we derive utility of notifications with different presentation levels. We then model the selection and delivery of rich notifications as an optimization problem with a goal to maximize the utility of notifications under resource budget constraints. We validate our system with large-scale simulations driven by the real-world de-identified traces obtained from Spotify. With the help of several baseline approaches we show that our solution is adaptive and resource efficient.