research-article

Exploiting User Context and Network Information for Mobile Application Usage Prediction

Authors:

Konglin Zhu,

Xiaoyi Zhang,

Bin Xiang,

Lin ZhangAuthors Info & Claims

HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking

Pages 25 - 30

https://doi.org/10.1145/2757513.2757519

Published: 22 June 2015 Publication History

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Abstract

The explosive increasing mobile Applications (Apps) have been attracting researchers and developers to investigate user preferences on various mobile Apps. Understanding mobile Apps usage pattern of end users will help to improve the quality of mobile Apps and meanwhile enhance the quality of experience of users. For instance, if the mobile device knows the App the user will launch, it can pre-load the App into memory and also put the quick launch icon on the home screen to speed up the App usage. In this paper, we collect mobile data from over 10,000 users and study the mobile Apps usage pattern from user perspective using the collected data on mobile devices, including time, location, last used App, network type, network speed and etc. We then use Naive Bayes and linear model to propose mobile App usage prediction method for the prediction of the next launched App by individuals. The result shows that the proposed App usage prediction method can reach 60% accuracy to predict the mobile App that will be launched by users.

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Cited By

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Meegahapola LRuiz-Correa SRobledo-Valero VHernandez-Huerfano EAlvarez-Rivera LChenu-Abente RGatica-Perez D(2021)One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-ReportsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481205:1(1-28)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448120
Liao ZZhao JLi Y(2019)Prediction of Mobile App Session TimeProceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3312662.3312695(52-55)Online publication date: 12-Jan-2019
https://dl.acm.org/doi/10.1145/3312662.3312695
Zhao JLi YWu JLiao Q(2018)Predict what app to use next time only consider time and latest used app contextProceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3180374.3181345(163-166)Online publication date: 13-Jan-2018
https://dl.acm.org/doi/10.1145/3180374.3181345
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Index Terms

Exploiting User Context and Network Information for Mobile Application Usage Prediction
1. Applied computing

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Published In

HOTPOST '15: Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking

June 2015

62 pages

ISBN:9781450335171

DOI:10.1145/2757513

General Chairs:
Michael Sirivianos
Cyprus University of Technology, Cyprus
,
Pan Hui
Hong Kong University of Science and Technology, Hong Kong
,
Nishanth Sastry
King's College London, UK
,
Program Chairs:
Yang Chen
Fudan University, China
,
Hongqiang Harry Liu
Microsoft Research, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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Funding Sources

EU FP7 IRSES Mobile Cloud Project
National Natural Science Foundation of China
the 111 Project

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MobiHoc'15

Sponsor:

SIGMOBILE

MobiHoc'15: The Sixteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

June 22, 2015

Hangzhou, China

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HOTPOST '15 Paper Acceptance Rate 5 of 10 submissions, 50%;

Overall Acceptance Rate 5 of 10 submissions, 50%

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Meegahapola LRuiz-Correa SRobledo-Valero VHernandez-Huerfano EAlvarez-Rivera LChenu-Abente RGatica-Perez D(2021)One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-ReportsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34481205:1(1-28)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448120
Liao ZZhao JLi Y(2019)Prediction of Mobile App Session TimeProceedings of the 2019 3rd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3312662.3312695(52-55)Online publication date: 12-Jan-2019
https://dl.acm.org/doi/10.1145/3312662.3312695
Zhao JLi YWu JLiao Q(2018)Predict what app to use next time only consider time and latest used app contextProceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences10.1145/3180374.3181345(163-166)Online publication date: 13-Jan-2018
https://dl.acm.org/doi/10.1145/3180374.3181345
Tan YYu KWu XPan DLiu Y(2018)Predicting App Usage Based on Link Prediction in User-App Bipartite NetworkSmart Computing and Communication10.1007/978-3-319-73830-7_20(191-205)Online publication date: 18-Jan-2018
https://doi.org/10.1007/978-3-319-73830-7_20
Chen YYao JJin HHe CChen H(2017)Exploring the Evolution of New Mobile ServicesScientific Programming10.1155/2017/51596902017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/5159690
Xiang CLiu DLi SZhu XLi YRen JLiang L(2017)HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems2017 IEEE Trustcom/BigDataSE/ICESS10.1109/Trustcom/BigDataSE/ICESS.2017.312(776-783)Online publication date: Aug-2017
https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.312
Zhu KZhang LPattavina A(2017)Learning Geographical and Mobility Factors for Mobile Application RecommendationIEEE Intelligent Systems10.1109/MIS.2017.5232:3(36-44)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1109/MIS.2017.52

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Understanding and prediction of mobile application usage for smart phones

Predicting mobile application usage using contextual information

Cohort Modeling Based App Category Usage Prediction

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