  With the emergence of mobile and wearable devices, push notification becomes
a powerful tool to connect and maintain the relationship with App users, but
sending inappropriate or too many messages at the wrong time may result in the
App being removed by the users. In order to maintain the retention rate and the
delivery rate of advertisement, we adopt Deep Neural Network (DNN) to develop a
pop-up recommendation system "Click sequence-aware deeP neural network
(DNN)-based Pop-uPs recOmmendation (C-3PO)" enabled by collaborative
filtering-based hybrid user behavioral analysis. We further verified the system
with real data collected from the product Security Master, Clean Master and CM
Browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In
this way, we can know precisely about users' preference and frequency to click
on the push notification/pop-ups, decrease the troublesome to users
efficiently, and meanwhile increase the click through rate of push
notifications/pop-ups.
