research-article

Characterizing Smartphone Usage Patterns from Millions of Android Users

Authors:

Felix Xiaozhu Lin,

Qiaozhu Mei, and

Feng FengAuthors Info & Claims

IMC '15: Proceedings of the 2015 Internet Measurement Conference

October 2015

Pages 459 - 472

https://doi.org/10.1145/2815675.2815686

Published: 28 October 2015 Publication History

Abstract

he prevalence of smart devices has promoted the popular- ity of mobile applications (a.k.a. apps) in recent years. A number of interesting and important questions remain unan- swered, such as why a user likes/dislikes an app, how an app becomes popular or eventually perishes, how a user selects apps to install and interacts with them, how frequently an app is used and how much traffic it generates, etc. This paper presents an empirical analysis of app usage behaviors collected from millions of users of Wandoujia, a leading An- droid app marketplace in China. The dataset covers two types of user behaviors of using over 0.2 million Android apps, including (1) app management activities (i.e., installa- tion, updating, and uninstallation) of over 0.8 million unique users and (2) app network traffic from over 2 million unique users. We explore multiple aspects of such behavior data and present interesting patterns of app usage. The results provide many useful implications to the developers, users, and disseminators of mobile apps.

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Index Terms

Characterizing Smartphone Usage Patterns from Millions of Android Users

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cover image ACM Conferences

IMC '15: Proceedings of the 2015 Internet Measurement Conference

October 2015

550 pages

ISBN:9781450338486

DOI:10.1145/2815675

General Chairs:
Kenjiro Cho
IIJ/Keio University
,
Kensuke Fukuda
NII/Sokendai
,
Program Chairs:
Vivek Pai
Google
,
Neil Spring
University of Maryland

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGCOMM: ACM Special Interest Group on Data Communication
USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2015

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Funding Sources

National Science Foundation
Natural Science Foundation of China
Ministry of Science and Technology China

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IMC '15

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SIGMETRICS
SIGCOMM
USENIX Assoc

IMC '15: Internet Measurement Conference

October 28 - 30, 2015

Tokyo, Japan

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IMC '15 Paper Acceptance Rate 31 of 96 submissions, 32%;

Overall Acceptance Rate 277 of 1,083 submissions, 26%

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November 4 - 6, 2024

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https://doi.org/10.1007/s42421-024-00089-y
Guo LChoe WLin FAamodt TJerger NSwift M(2023)STI: Turbocharge NLP Inference at the Edge via Elastic PipeliningProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575698(791-803)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575698
Tian YZhou KPelleg D(2023)Characterization and Prediction of Mobile TasksACM Transactions on Information Systems10.1145/352271141:1(1-39)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3522711
Li TFan YLi YTarkoma SHui P(2023)Understanding the Long-Term Evolution of Mobile App UsageIEEE Transactions on Mobile Computing10.1109/TMC.2021.309866422:2(1213-1230)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3098664
Chen CTso GHe K(2023)Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financingComputational Economics10.1007/s10614-023-10365-863:2(919-950)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10614-023-10365-8
Fazelnia GSimon EAnderson ICarterette BLalmas MSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Variational User Modeling with Slow and Fast FeaturesProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498477(271-279)Online publication date: 11-Feb-2022
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Zhao SPan GTao JLuo ZLi SWu Z(2022)Understanding Smartphone Users From Installed App Lists Using Boolean Matrix FactorizationIEEE Transactions on Cybernetics10.1109/TCYB.2020.296764452:1(384-397)Online publication date: Jan-2022
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Dammak HMejri ORiahi MMoussa F(2022)User Group Profiling through Mobile Application Usage Behavior2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)10.1109/SETIT54465.2022.9875502(278-285)Online publication date: 28-May-2022
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Li TXia TWang HTu ZTarkoma SHan ZHui P(2022)Smartphone App Usage Analysis: Datasets, Methods, and ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.316317624:2(937-966)Online publication date: Oct-2023
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Lee JSchroeder JEpstein D(2021)Understanding and Supporting Self-Tracking App SelectionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949805:4(1-25)Online publication date: 30-Dec-2021
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