C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation: I know you’ll click

Ton Ton Hsien De Huang, Hung Yu Kao

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)11793-11799
Number of pages7
JournalSoft Computing
Issue number22
Publication statusPublished - 2019 Nov 1

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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