research-article

Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns

Authors:

Giuseppe Cardone,

Andrew Campbell,

Tanzeem ChoudhuryAuthors Info & Claims

ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers

Pages 69 - 76

https://doi.org/10.1145/2493988.2494333

Published: 08 September 2013 Publication History

Abstract

Reliable smartphone app prediction can strongly benefit both users and phone system performance alike. However, real-world smartphone app usage behavior is a complex phenomena driven by a number of competing factors. In this pa- per, we develop an app usage prediction model that leverages three key everyday factors that affect app usage decisions -- (1) intrinsic user app preferences and user historical patterns; (2) user activities and the environment as observed through sensor-based contextual signals; and, (3) the shared aggregate patterns of app behavior that appear in various user communities. While rapid progress has been made recently in smartphone app prediction, existing prediction models tend to focus on only one of these factors. We evaluate a multi-faceted approach to prediction using (1) a 3-week 35-user field trial, along with (2) analysis of app usage logs of 4,606 smartphone users worldwide. We find our app usage model can not only produce more robust app predictions than conventional techniques, but it can also enable significant smartphone system optimizations.

References

[1]

MTA Subway Time. http://apps.mta.info/traintime/.

[2]

Runkeeper. http://runkeeper.com/.

[3]

R. Bridle and E. McCreath., Predictive Menu Selection on a Mobile Phone, In ECML'05. pp.75--88, 2005

[4]

C. Shin, J.-H. Hong, A. Dey., Understanding and Prediction of Mobile Application Usage for Smart Phones, In Ubicomp'12. pp.173--182, 2012.

Digital Library

[5]

5. C. Yoon, et al., AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring, In ATC'12. pp.1--14, 2012.

Digital Library

[6]

T. Yan et al., Fast App Launching for Mobile Devices using Predictive User Context, In MobiSys'12. pp.113--126, 2012.

Digital Library

[7]

K. Shi and K. Ali., GetJar mobile application recommendations with very sparse datasets, In KDD'12. pp.204--212, 2012.

Digital Library

[8]

D. Peebles, et al., Community-guided learning: Exploiting mobile sensor users to model human behavior, In AAAI'10. pp.1600--1606, 2010.

[9]

H. Lu et al., The Jigsaw continuous sensing engine for mobile phone applications, In Sensys'10. pp.71--84, 2010.

Digital Library

[10]

B. Yan and G. Chen., AppJoy: personalized mobile application discovery, In MobiSys'11. pp.113--126, 2011.

Digital Library

[11]

J. Shawe-Taylor and N. Cristianini., Kernel Methods for Pattern Analysis, Cambridge Press, 2004.

Digital Library

[12]

W. Ping, et al., FAMER: Making Multi-Instance Learning Better and Faster, In SDM'11. pp. 594--605, 2011.

[13]

Y. Xu, et al., Multi-instance Metric Learning, In ICDM'11. pp.874--883, 2011.

Digital Library

[14]

S. Lyu., Mercer kernels for object recognition with local features, In CVPR'05. pp.223--229, 2005.

Digital Library

[15]

J. T. Kwok and P.-M. Cheung., Marginalized multi-instance kernels, In IJCAI'07. pp.901--906, 2007.

Digital Library

[16]

J. Shao., Mathematical statistics, New York Springer-Verlag, 1998.

[17]

C. M. Bishop., Pattern Recognition and Machine Learning, Springer, 2006.

Digital Library

[18]

W. Pan, N. Aharony and A. Pentland., Composite Social Network for Predicting Mobile Apps Installation, In AAAI'11. pp.821--827, 2007.

[19]

H. Verkasalo., Contextual patterns in mobile service usage, PUC, 13(5):331--342, 2009.

Digital Library

[20]

A. Woznica and A. Kalousis., Adaptive distances on sets of vectors, In ICDM'10. pp.579--588, 2010.

Digital Library

[21]

B. Matthias et al., Falling asleep with Angry Birds, Facebook and Kindle: a large scale study on mobile application usage, In MobileHCI'11. pp.47--56, 2011.

Digital Library

[22]

R. Ahmad et al., Exploring iPhone usage: the influence of socioeconomic differences on smartphone adoption, usage and usability, In MobileHCI'12. pp.11--20, 2012.

Digital Library

[23]

T. Minh et al., Smartphone usage in the wild: a large-scale analysis of applications and context. In ICMI'11. pp.353--360, 2011.

Digital Library

[24]

O. Franko and T. Tirrell., Smartphone app use among medical providers in ACGME training programs. Journal of Medical Systems, 36: pp.3135--3139, 2011.

Digital Library

[25]

J. S. Breese, D. Heckerman, and C. Kadie., Empirical analysis of predictive algorithms for collaborative filtering, In UAI'98. pp.43--52, 1998.

Digital Library

[26]

J. L. Herlocker et al., Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):pp.5--53, 2004.

Digital Library

[27]

A. Popescul, D. M. Pennock, and S. Lawrence., Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments, In UAI'01. pp.437--444, 2001.

Digital Library

[28]

N. Lane et al., Enabling large-scale human activity inference on smartphones using community similarity networks, In UbiComp'11. pp.355--364, 2011.

Digital Library

[29]

N. Lane et al., Exploiting social networks for large-scale human behavior modeling, IEEE Pervasive Computing, 10(4):pp.45--53, 2011.

Digital Library

Cited By

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
https://dl.acm.org/doi/10.1145/3643514
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
https://doi.org/10.1109/THMS.2024.3412273
Fang XYang HDing DGao WZhang LWang YShi L(2024)Enhancing App Usage Prediction Accuracy With GCN-Transformer Model and Meta-Path ContextIEEE Access10.1109/ACCESS.2024.337239712(53031-53044)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3372397
Show More Cited By

Index Terms

Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Understanding and prediction of mobile application usage for smart phones
UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

It is becoming harder to find an app on one's smart phone due to the increasing number of apps available and installed on smart phones today. We collect sensory data including app use from smart phones, to perform a comprehensive analysis of the context ...
Improving Application Launch Performance in Smartphones Using Recurrent Neural Network
ICMLT '18: Proceedings of the 2018 International Conference on Machine Learning Technologies

Mobile phones became indispensable tools in our lives, with Android being the most used mobile OS. These devices depend on managing application lifecycles to improve launch performance, but the management of application processes is not done in an ...
Prophet: what app you wish to use next
UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

A variety of applications (app) installed on smart phones do greatly enrich our lives, but make it more difficult to organize our screens and folders. Predicting apps that will be in use next can benefit users a lot. In this poster, we propose some ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers

September 2013

160 pages

ISBN:9781450321273

DOI:10.1145/2493988

General Chair:
Kristof Van Laerhoven
TU Darmstadt, Germany
,
Program Chairs:
Daniel Roggen
Newcastle University, UK
,
Daniel Gatica-Perez
Idiap and EPFL, Switzerland
,
Masaaki Fukumoto
Microsoft Research Asia, China

Copyright © 2013 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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

ISWC '13 Paper Acceptance Rate 20 of 101 submissions, 20%;

Overall Acceptance Rate 38 of 196 submissions, 19%

Upcoming Conference

UbiComp '24

Sponsor:
sigchi
sigchi

The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

95
Total Citations
View Citations
1,895
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)3

Reflects downloads up to 13 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
https://dl.acm.org/doi/10.1145/3643514
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
https://doi.org/10.1109/THMS.2024.3412273
Fang XYang HDing DGao WZhang LWang YShi L(2024)Enhancing App Usage Prediction Accuracy With GCN-Transformer Model and Meta-Path ContextIEEE Access10.1109/ACCESS.2024.337239712(53031-53044)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3372397
Saiapin ABalitskiy GBershatsky DKatrutsa AFrolov EFrolov AOseledets IKharin V(2024)Federated privacy-preserving collaborative filtering for on-device next app predictionUser Modeling and User-Adapted Interaction10.1007/s11257-024-09395-0Online publication date: 28-Mar-2024
https://doi.org/10.1007/s11257-024-09395-0
De Masi AWac K(2023)Forecasting Smartphone Application Chains: an App-Rank Based ApproachProceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia10.1145/3626705.3627802(87-98)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1145/3626705.3627802
Khaokaew YSalim FXue H(2023)Understanding Mobile Information Needs and BehavioursAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610760(210-214)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610760
Yang KZhao XZou JDu W(2023) ATPP: A Mobile App Prediction System Based on Deep Marked Temporal Point ProcessesACM Transactions on Sensor Networks10.1145/358255519:3(1-24)Online publication date: 5-Apr-2023
https://dl.acm.org/doi/10.1145/3582555
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
Song TGweon G(2023)Predicting Next Application Most Likely Used with Word Embedding and Time-Series Data Encoding2023 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp57234.2023.00051(277-284)Online publication date: Feb-2023
https://doi.org/10.1109/BigComp57234.2023.00051
Tomlinson KBenson A(2023)Graph-based methods for discrete choiceNetwork Science10.1017/nws.2023.20(1-20)Online publication date: 6-Nov-2023
https://doi.org/10.1017/nws.2023.20
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents