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

TL;DR

This paper introduces C-3PO, a deep neural network-based system that personalizes push notification pop-up recommendations by analyzing user click behavior, aiming to improve user retention and engagement while reducing annoyance.

Contribution

The paper presents a novel click-sequence-aware DNN model for pop-up recommendation, integrating collaborative filtering and real user data for personalized notifications.

Findings

Increased click-through rates for push notifications.

Reduced user annoyance and app uninstallation.

Validated effectiveness with real-world data from multiple apps.

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.