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Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications

Lopez-Tovar, H; Charalambous, A; Dowell, J; (2015) Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications. In: Brdiczka, O and Chau, P and Carenini, G and Pan, S and Kristensson, PO, (eds.) IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces. (pp. pp. 296-299). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

Smartphones are capable of alerting their users to different kinds of digital interruption using different modalities and with varying modulation. Smart notification is the capability of a smartphone for selecting the user's preferred kind of alert in particular situations using the full vocabulary of notification modalities and modulations. It therefore goes well beyond attempts to predict if or when to silence a ringing phone call. We demonstrate smart notification for messages received from a document retrieval system while the user is attending a meeting. The notification manager learns about their notification preferences from users' judgements about videos of meetings. It takes account of the relevance of the interruption to the meeting, whether the user is busy and the sensed location of the smartphone. Through repeated training, the notification manager learns to reliably predict the preferred notification modes for users and this learning continues to improve with use.

Type: Proceedings paper
Title: Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications
Event: 20th International Conference on Intelligent User Interfaces (IUI '15)
Location: Atlanta
ISBN-13: 9781450333061
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2678025.2701390
Publisher version: https://doi.org/10.1145/2678025.2701390
Language: English
Additional information: Copyright © 2015 ACM.
Keywords: Adaptation; Smartphone notification; Interruption; Alert modality; Machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1465319
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