research-article

Large-scale evaluation of call-availability prediction

Author:

Martin PielotAuthors Info & Claims

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 933 - 937

https://doi.org/10.1145/2632048.2632060

Published: 13 September 2014 Publication History

Abstract

We contribute evidence to which extent sensor- and contextual information available on mobile phones allow to predict whether a user would pick up a call or not. Using an app publicly available for Android phones, we logged anonymous data from 31311 calls of 418 different users. The data shows that information easily available in mobile phones, such as the time since the last call, the time since the last ringer mode change, or the device posture, can predict call availability with an accuracy of 83.2% (Kappa = .646). Personalized models can increase the accuracy to 87% on average. Features related to when the user was last active turned out to be strong predictors. This shows that simple contextual cues approximating user activity are worthwhile investigating when designing context-aware ubiquitous communication systems.

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Cited By

Terzimehic NBemmann FHalsner MMayer S(2023)A Mixed-Method Exploration into the Mobile Phone Rabbit HoleProceedings of the ACM on Human-Computer Interaction10.1145/36042417:MHCI(1-29)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604241
Khan MLu LAfzal HNasir J(2023)PredictionMiner: mining the latest individual behavioral rules for personalized contextual pattern predictionsSoft Computing10.1007/s00500-023-08572-4Online publication date: 17-Jul-2023
https://doi.org/10.1007/s00500-023-08572-4
Sarker IKhan AAbushark YAlsolami F(2021)Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile ApplicationsSymmetry10.3390/sym1310197513:10(1975)Online publication date: 19-Oct-2021
https://doi.org/10.3390/sym13101975
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Index Terms

Large-scale evaluation of call-availability prediction
1. Human-centered computing

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Published In

cover image ACM Conferences

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2014

973 pages

ISBN:9781450329682

DOI:10.1145/2632048

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Copyright © 2014 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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Publication History

Published: 13 September 2014

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UbiComp '14

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UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

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

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Cited By

Terzimehic NBemmann FHalsner MMayer S(2023)A Mixed-Method Exploration into the Mobile Phone Rabbit HoleProceedings of the ACM on Human-Computer Interaction10.1145/36042417:MHCI(1-29)Online publication date: 13-Sep-2023
https://dl.acm.org/doi/10.1145/3604241
Khan MLu LAfzal HNasir J(2023)PredictionMiner: mining the latest individual behavioral rules for personalized contextual pattern predictionsSoft Computing10.1007/s00500-023-08572-4Online publication date: 17-Jul-2023
https://doi.org/10.1007/s00500-023-08572-4
Sarker IKhan AAbushark YAlsolami F(2021)Mobile Expert System: Exploring Context-Aware Machine Learning Rules for Personalized Decision-Making in Mobile ApplicationsSymmetry10.3390/sym1310197513:10(1975)Online publication date: 19-Oct-2021
https://doi.org/10.3390/sym13101975
Mehrotra APejovic VMusolesi M(2021)FutureWare: Designing a Middleware for Anticipatory Mobile ComputingIEEE Transactions on Software Engineering10.1109/TSE.2019.294355447:10(2107-2124)Online publication date: 1-Oct-2021
https://doi.org/10.1109/TSE.2019.2943554
Sarker IColman AHan JWatters PSarker IColman AHan JWatters P(2021)Contextual Mobile Datasets, Pre-processing and Feature SelectionContext-Aware Machine Learning and Mobile Data Analytics10.1007/978-3-030-88530-4_4(59-73)Online publication date: 20-Sep-2021
https://doi.org/10.1007/978-3-030-88530-4_4
Mehrotra AMusolesi M(2020)Intelligent Notification SystemsSynthesis Lectures on Mobile and Pervasive Computing10.2200/S00965ED1V01Y201911MPC01411:1(1-75)Online publication date: 3-Jan-2020
https://doi.org/10.2200/S00965ED1V01Y201911MPC014
Sarker IAlqahtani HAlsolami FKhan AAbushark YSiddiqui M(2020)Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modelingJournal of Big Data10.1186/s40537-020-00328-37:1Online publication date: 23-Jul-2020
https://doi.org/10.1186/s40537-020-00328-3
Toch EChassidim HHatuka T(2020)Can you Turn it Off?Proceedings of the ACM on Human-Computer Interaction10.1145/34151624:CSCW2(1-18)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3415162
Sarker IHoque MUddin MAlsanoosy T(2020)Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research DirectionsMobile Networks and Applications10.1007/s11036-020-01650-z26:1(285-303)Online publication date: 14-Sep-2020
https://doi.org/10.1007/s11036-020-01650-z
Sarker I(2019)Context-aware rule learning from smartphone data: survey, challenges and future directionsJournal of Big Data10.1186/s40537-019-0258-46:1Online publication date: 31-Oct-2019
https://doi.org/10.1186/s40537-019-0258-4
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