RingLearn: Long-term mitigation of disruptive smartphone interruptions

Smith, Jeremiah; Dulay, Naranker

doi:10.1109/percomw.2014.6815160

Cited by 15 publications

(11 citation statements)

References 17 publications

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“…Fisher et al [20] built an in-context application for smartphone to create personalized interruptibility prediction model for phone calls. Although they achieved high prediction accuracy (96.12%), similar to other works [66,71,70], their model only predicts phone's ringer modes (on and off), which is a rough measurement of interruptibility. Moreover, the ringer mode may not reflect users' actual interruptibility, for example, when they forget to switch the mode when their interruptibility changes.…”

Section: Related Worksupporting

confidence: 56%

“…Rosenthal et al [66] used ESM to collect data and train personalized models to learn when to silence the phone to avoid embarrassing interruptions. Smith et al [71] considered dataset imbalance, error costs, user behaviors to recognize disruptive incoming calls, and developed RingLearn [70] to mitigate disruptive phone calls. Fisher et al [20] built an in-context application for smartphone to create personalized interruptibility prediction model for phone calls.…”

Section: Related Workmentioning

confidence: 99%

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How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Smartphones frequently notify users about newly available messages or other notifications. It can be very disruptive when these notifications interrupt users while they are busy. Our work here is based on the observation that people usually exhibit different levels of busyness at different contexts. This means that classifying users' interruptibility as a binary status, interruptible or not interruptible, is not sufficient to accurately measure their availability towards smartphone interruptions. In this paper, we propose, implement and evaluate a two-stage hierarchical model to predict people's interruptibility intensity. Our work is the first to introduce personality traits into interruptibility prediction model, and we found that personality data improves the prediction significantly. Our model bootstraps the prediction with similar people's data, and provides a good initial prediction for users whose individual models have not been trained on their own data yet. Overall prediction accuracy of our model can reach 66.1%.We present the following four major contributions: 1) We propose a two-stage hierarchical interruptibility prediction model. In the first stage, our model predicts (with 75% accuracy) whether a user will react to an interruption

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Section: Related Worksupporting

confidence: 56%

Section: Related Workmentioning

confidence: 99%

How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…The App shown in Figure 1 had a single screen when opened in the Android App launcher which explained the purpose and how to use the app, while a broadcast receiver would launch the above mentioned custom incoming call screen with a small customisable delay to overlay on the Android default incoming screen. A more detailed explanation of the App can be found in [17]. The collected features were: day of week, month, incoming call time, incoming phone number (-1 if unavailable), cell tower id, and WiFi SSID.…”

Section: Collected Datamentioning

confidence: 99%

Learning to recognise disruptive smartphone notifications

Smith

Lavygina

et al. 2014

Proceedings of the 16th International Conference on Human-Computer Interaction With Mobile Devices &Amp; Services

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Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive smartphone notifications longer-term, taking into account varying error costs. In this paper we report on a 16 week field study comparing how well different learners perform at mitigating disruptive incoming phone calls.

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“…Approaches have also been demonstrated that mitigate the interruption caused by phone calls by silencing the notification but leaving the visual alert. The Ringlearn system [9] learned preferences for when to silence phone call notifications in different settings whilst the In-Context system [3] learnt when to silence phone call notifications taking account of different levels of user interruptibility.…”

Section: Introductionmentioning

confidence: 99%

“…For example, an interruption from a social network will often require a more nuanced notification than a phone call. Rather than simply a binary choice of alert now or later, or of let ring or silence [9], notifications require more carefully calibrated alert cues in visual, auditory and tactile modalities.…”

Section: Introductionmentioning

confidence: 99%

Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications

Lopez-Tovar

Charalambous

Dowell

2015

Proceedings of the 20th International Conference on Intelligent User Interfaces

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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.

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RingLearn: Long-term mitigation of disruptive smartphone interruptions

Cited by 15 publications

References 17 publications

How Busy Are You?

How Busy Are You?

Learning to recognise disruptive smartphone notifications

Managing Smartphone Interruptions through Adaptive Modes and Modulation of Notifications

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