Convolutional Neural Network / Deceptive AdvertisingDeep Neural Network / Phone ScamsConvolutional Neural Network / Android Malware

TonTon H.-D. Huang*, and Hung-Yu Kao, "C-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs RecOmmendation", arXiv:1803.00458

AI Improves the Frequency and Quality of Mobile App Notifications:

C-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs RecOmmendation

TonTon H.-D. Huang*, and Hung-Yu Kao**

Leopard Mobile (Cheetah Mobile Taiwan Agency)*
Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan* **
TonTon (at) TWMAN.ORG*

    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 "Clicksequence- aware deeP neural network (DNN)-based Pop-uPs recOmmendation (C-3PO)" enabled by collaborative filteringbased 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.