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C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation

I know you’ll click
  • TonTon Hsien-De HuangEmail author
  • Hung-Yu Kao
Methodologies and Application
  • 8 Downloads

Abstract

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.

Keywords

Click sequence aware Deep learning Deep neural network 

Notes

Acknowledgements

This work would not have been possible without the valuable dataset offered by Cheetah Mobile Inc.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency)TaipeiTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan

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