research-article

Predicting mobile application usage using contextual information

Authors:

Ke Huang,

Chunhui Zhang,

Xiaoxiao Ma,

Guanling ChenAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 1059 - 1065

https://doi.org/10.1145/2370216.2370442

Published: 05 September 2012 Publication History

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Abstract

As the mobile applications become increasing popular, people are installing more and more Apps on their smart phones. In this paper, we answer the question whether it is feasible to predict which App the user will open. The ability for such prediction can help pre-loading the right Apps to the memory for faster execution or help floating the desired Apps to the home screen for quicker launch. We explored a variety of contextual information, such as last used App, time, location, and the user profile, to predict the user's App usage using the MDC dataset. We present three findings from our studies. First, the contextual information can be used to learn the pattern of user's App usage and to predict App usage effectively. Second, for the MDC dataset, the correlation between sequentially used Apps has a strong contribution to the prediction accuracy. Lastly, the linear model is more effective than the Bayesian model to combine all contextual information and for such predictions.

References

[1]

D. Ashbrook and T. Starner. Using gps to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput., 7(5):275--286, Oct. 2003.

Digital Library

Google Scholar

[2]

A. Dix, T. Rodden, N. Davies, J. Trevor, A. Friday, and K. Palfreyman. Exploiting space and location as a design framework for interactive mobile systems. A CM Trans. Comput.-Hum. Interact., 7(3):285--321, Sept. 2000.

Digital Library

Google Scholar

[3]

T.-M.-T. Do and D. Gatica-Perez. By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia, MUM '10, pages 27:1--27:10, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[4]

H. W. Gellersen, A. Schmidt, and M. Beigi. Multi-sensor context-awareness in mobile devices and smart artifacts. Mob. Netw. Appl., 7(5):341--351, Oct. 2002.

Digital Library

Google Scholar

[5]

J.-M. Kang, S. seok Seo, and J.-K. Hong. Usage pattern analysis of smartphones. In Network Operations and Management Symposium (APNOMS), 2011 13th Asia-Pacific, pages 1--8, sept. 2011.

Crossref

Google Scholar

[6]

J. K. Laurila, D. Gatica-Perez, I. Aad, J. Blom, O. Bornet, T.-M.-T. Do, O. Dousse, J. Eberle, and M. Miettinen. The mobile data challenge: Big data for mobile computing research. In Mobile Data Challenge by Nokia Workshop, in conjunction with Int. Conf. on Pervasive Computing, June 2012.

Google Scholar

[7]

S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A. Campbell. Nextplace: A spatio-temporal prediction framework for pervasive systems. In K. Lyons, J. Hightower, and E. Huang, editors, Pervasive Computing, volume 6696 of Lecture Notes in Computer Science, pages 152--169. Springer Berlin/Heidelberg, 2011.

Digital Library

Google Scholar

[8]

B. Yan and G. Chen. Appjoy: personalized mobile application discovery. In Proceedings of the 9th international conference on Mobile systems, applications, and services, MobiSys '11, pages 113--126, New York, NY, USA, 2011. ACM.

Digital Library

Google Scholar

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Published In

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

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

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Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Wu YDu DXu CXia YFu MZang BChen HEeckhout LSmaragdakis GLiang KSampson AKim MRossbach C(2025)D-VSync: Decoupled Rendering and Displaying for Smartphone GraphicsProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3669940.3707235(326-341)Online publication date: 30-Mar-2025
https://dl.acm.org/doi/10.1145/3669940.3707235
Peters HLiu YBarbieri FBaten RMatz SBos M(2024)Context-aware prediction of active and passive user engagement: Evidence from a large online social platformJournal of Big Data10.1186/s40537-024-00955-011:1Online publication date: 8-Aug-2024
https://doi.org/10.1186/s40537-024-00955-0
Li THui SZhang SWang HZhang YHui PJin DLi Y(2024)Mobile User Traffic Generation Via Multi-Scale Hierarchical GANACM Transactions on Knowledge Discovery from Data10.1145/366465518:8(1-19)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3664655
Khaokaew YXue HSalim F(2024)MAPLEProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435148:1(1-25)Online publication date: 6-Mar-2024
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Fang XYang HShi LWang YLi L(2024)BERT-Based Semantic-Aware Heterogeneous Graph Embedding Method for Enhancing App Usage Prediction AccuracyIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.341227354:4(465-474)Online publication date: Aug-2024
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