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ATPP: A Mobile App Prediction System Based on Deep Marked Temporal Point Processes

Published: 05 April 2023 Publication History

Abstract

Predicting the next application (app) a user will open is essential for improving the user experience, e.g., app pre-loading and app recommendation. Unlike previous solutions that only predict which app the user will open, this article predicts both the next app and the time to open it. Time prediction is essential to avoid loading the next app too early and consuming unnecessary resources on smartphones. To predict the next app and open time jointly, we model the app usage sequence as a marked temporal point process (MTPP), whose conditional intensity function can capture the probability of a new app usage event. We develop a novel data-driven MTPP-based app prediction system, named ATPP (App Temporal Point Process), which adopts a recurrent neural network architecture to learn the MTPP conditional intensity function for app prediction. ATPP adopts a set of techniques to incorporate the unique features of app prediction in our RNN architecture, including learning the correlated usage behavior of different apps by representation learning, the temporal dependency of app usage by an attention mechanism, and the location-related app usage behavior by feature extraction and fusion layer. We conduct extensive experiments on a large-scale anonymized app usage dataset to verify ATPP’s effectiveness.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 19, Issue 3
August 2023
597 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3584865
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 05 April 2023
Online AM: 31 January 2023
Accepted: 15 December 2022
Revised: 16 July 2022
Received: 05 November 2021
Published in TOSN Volume 19, Issue 3

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Author Tags

  1. Mobile devices
  2. application usage prediction
  3. marked temporal point process
  4. recurrent neural networks

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