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

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)11793-11799
頁數7
期刊Soft Computing
23
發行號22
DOIs
出版狀態Published - 2019 十一月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 理論電腦科學
  • 幾何和拓撲

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