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What and How long: Prediction of Mobile App Engagement

Authors:

Dan PellegAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 1

Article No.: 8, Pages 1 - 38

https://doi.org/10.1145/3464301

Published: 08 September 2021 Publication History

Abstract

User engagement is crucial to the long-term success of a mobile app. Several metrics, such as dwell time, have been used for measuring user engagement. However, how to effectively predict user engagement in the context of mobile apps is still an open research question. For example, do the mobile usage contexts (e.g., time of day) in which users access mobile apps impact their dwell time? Answers to such questions could help mobile operating system and publishers to optimize advertising and service placement. In this article, we first conduct an empirical study for assessing how user characteristics, temporal features, and the short/long-term contexts contribute to gains in predicting users’ app dwell time on the population level. The comprehensive analysis is conducted on large app usage logs collected through a mobile advertising company. The dataset covers more than 12K anonymous users and 1.3 million log events. Based on the analysis, we further investigate a novel mobile app engagement prediction problem—can we predict simultaneously what app the user will use next and how long he/she will stay on that app? We propose several strategies for this joint prediction problem and demonstrate that our model can improve the performance significantly when compared with the state-of-the-art baselines. Our work can help mobile system developers in designing a better and more engagement-aware mobile app user experience.

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Cited By

Griefahn AAvermann FZalpour CMarshall RCordon Morillas ILuedtke K(2025)Exploring the Effect of an 8-Week AI-Composed Exercise Program on Pain Intensity and Well-Being in Patients With Spinal Pain: Retrospective Cohort AnalysisJMIR Formative Research10.2196/578269(e57826-e57826)Online publication date: 18-Feb-2025
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Sun CChen JZhao YHan HJing RTan GWu D(2025)Appformer: A novel framework for mobile app usage prediction leveraging progressive multi-modal data fusion and feature extractionExpert Systems with Applications10.1016/j.eswa.2024.125903265(125903)Online publication date: Mar-2025
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Index Terms

What and How long: Prediction of Mobile App Engagement
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 1

January 2022

599 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3483337

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Received: 01 October 2021

Published: 08 September 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Published in TOIS Volume 40, Issue 1

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