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 journalArticle

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.

Original languageEnglish
JournalSoft Computing
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Fingerprint

Application programs
Recommendations
Neural Networks
Collaborative filtering
Recommender systems
Recommendation System
Collaborative Filtering
User Preferences
Taiwan
Decrease
Deep neural networks

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

Cite this

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C-3PO : Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation: I know you’ll click. / Huang, Ton Ton Hsien De; Kao, Hung-Yu.

In: Soft Computing, 01.01.2019.

Research output: Contribution to journalArticle

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