research-article

Beyond Interruptibility: Predicting Opportune Moments to Engage Mobile Phone Users

Authors:

Kleomenis Katevas,

Aleksandar Matic,

Nuria OliverAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 91, Pages 1 - 25

https://doi.org/10.1145/3130956

Published: 11 September 2017 Publication History

Abstract

Many of today's mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products' and users' expectations. To address this challenge, we aim at developing an intelligent mobile system that automatically infers moments in which users are open to engage with suggested content. To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire. In this study, however, we analyze whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In addition, the study app logged a wide range of data related to their phone use. Based on 120 Million phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that before delivering a notification predicts whether a participant will click on the notification and subsequently engage with the offered content. When compared to a naïve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user's past behavior, predictions improve 5-fold over the baseline. Based on these findings, we discuss the implications for building an intelligent service that identifies opportune moments for proactive user engagement, while, at the same time, reduces the number of undesirable interruptions.

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https://doi.org/10.3390/s24082612
Lin YSu LChen ULee YWang PChang Y(2024)Pinning, Sorting, and Categorizing Notifications: A Mixed-methods Usage and Experience Study of Mobile Notification-management FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785798:3(1-27)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678579
Lee YWu MChang CChang XChang Y(2024)Investigating User-perceived Impacts of Contextual Factors on Opportune MomentsProceedings of the ACM on Human-Computer Interaction10.1145/36765148:MHCI(1-28)Online publication date: 24-Sep-2024
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Index Terms

Beyond Interruptibility: Predicting Opportune Moments to Engage Mobile Phone Users
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

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Publication History

Published: 11 September 2017

Accepted: 01 June 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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https://doi.org/10.3390/s24082612
Lin YSu LChen ULee YWang PChang Y(2024)Pinning, Sorting, and Categorizing Notifications: A Mixed-methods Usage and Experience Study of Mobile Notification-management FeaturesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785798:3(1-27)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678579
Lee YWu MChang CChang XChang Y(2024)Investigating User-perceived Impacts of Contextual Factors on Opportune MomentsProceedings of the ACM on Human-Computer Interaction10.1145/36765148:MHCI(1-28)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676514
Ji HYang HLi LZhang SZhang CLi XOu WGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)TIM: Temporal Interaction Model in Notification SystemProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657614(1120-1124)Online publication date: 30-May-2024
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Kim MLee JKoh YLee CLee UKim A(2024)Interrupting for Microlearning: Understanding Perceptions and Interruptibility of Proactive Conversational Microlearning ServicesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642778(1-21)Online publication date: 11-May-2024
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