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

Published: 04 August 2017 Publication History

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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  • (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
  • (2020)Intelligent Notification SystemsSynthesis Lectures on Mobile and Pervasive Computing10.2200/S00965ED1V01Y201911MPC01411:1(1-75)Online publication date: 3-Jan-2020
  • (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
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  1. Interpretable Machine Learning for Mobile Notification Management: An Overview of PrefMiner

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    Published In

    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
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 August 2017
    Published in SIGMOBILE-GETMOBILE Volume 21, Issue 2

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    • (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
    • (2020)Intelligent Notification SystemsSynthesis Lectures on Mobile and Pervasive Computing10.2200/S00965ED1V01Y201911MPC01411:1(1-75)Online publication date: 3-Jan-2020
    • (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
    • (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
    • (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
    • (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
    • (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

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