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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.

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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)

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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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

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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%

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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/s11257-024-09395-034:4(1369-1398)Online 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
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