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Interpretable Machine Learning for Mobile Notification Management: An Overview of PrefMiner

Authors:

Abhinav Mehrotra,

Robert Hendley,

Mirco MusolesiAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 21, Issue 2

Pages 35 - 38

https://doi.org/10.1145/3131214.3131225

Published: 04 August 2017 Publication History

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Abstract

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, for this reason, they are considered disruptive and potentially annoying, as well. PrefMiner is a novel interruptibility management solution that learns users' preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. PrefMiner aims at being intelligible and interpretable for users, i.e., not just a "black box" solution, by suggesting rules to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.

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Cited By

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Veerappa MAnneken MBurkart NHuber M(2022)Validation of XAI explanations for multivariate time series classification in the maritime domainJournal of Computational Science10.1016/j.jocs.2021.10153958(101539)Online publication date: Feb-2022
https://doi.org/10.1016/j.jocs.2021.101539
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
Fraser KConlan OTentori MWeibel NVan Laerhoven KAbowd GSalim F(2020)Enticing notification text & the impact on engagementAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414430(444-449)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3410530.3414430
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Interpretable Machine Learning for Mobile Notification Management: An Overview of PrefMiner
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cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 21, Issue 2

June 2017

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3131214

Editor:
Eyal de Lara
University of Toronto

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Association for Computing Machinery

New York, NY, United States

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Published: 04 August 2017

Published in SIGMOBILE-GETMOBILE Volume 21, Issue 2

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Veerappa MAnneken MBurkart NHuber M(2022)Validation of XAI explanations for multivariate time series classification in the maritime domainJournal of Computational Science10.1016/j.jocs.2021.10153958(101539)Online publication date: Feb-2022
https://doi.org/10.1016/j.jocs.2021.101539
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
Fraser KConlan OTentori MWeibel NVan Laerhoven KAbowd GSalim F(2020)Enticing notification text & the impact on engagementAdjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers10.1145/3410530.3414430(444-449)Online publication date: 10-Sep-2020
https://dl.acm.org/doi/10.1145/3410530.3414430
Wen YFu LLi GMa JMa H(2020)Accelerated Discovery of Potential Organic Dyes for Dye‐Sensitized Solar Cells by Interpretable Machine Learning Models and Virtual ScreeningSolar RRL10.1002/solr.2020001104:6Online publication date: 24-Apr-2020
https://doi.org/10.1002/solr.202000110
Sutton RFraser KConlan O(2019)A Reinforcement Learning and Synthetic Data Approach to Mobile Notification ManagementProceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia10.1145/3365921.3365932(155-164)Online publication date: 2-Dec-2019
https://dl.acm.org/doi/10.1145/3365921.3365932
Coto FLopez Herrera GQuiros LRamirez Benavides K(2019)Scheduling of events through notifications in mobile devices2019 IV Jornadas Costarricenses de Investigación en Computación e Informática (JoCICI)10.1109/JoCICI48395.2019.9105316(1-6)Online publication date: Aug-2019
https://doi.org/10.1109/JoCICI48395.2019.9105316
Anderson CHübener ISeipp AOhly SDavid KPejovic V(2018)A Survey of Attention Management Systems in Ubiquitous Computing EnvironmentsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32142612:2(1-27)Online publication date: 5-Jul-2018
https://dl.acm.org/doi/10.1145/3214261

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Designing content-driven intelligent notification mechanisms for mobile applications

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