Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users

Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users
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立即提醒或永不提醒:了解和预测智能手机用户的通知偏好

DOI:
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发表时间:
2022
期刊:
ACM Trans. Comput. Hum. Interact.
影响因子:
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通讯作者:
Jeffrey Nichols
Jeffrey Nichols
中科院分区:
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文献类型:
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作者:
Tianshi Li;J. Haines;Miguel Flores Ruiz De Eguino;Jason I. Hong;Jeffrey Nichols

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通知是移动设备不可或缺的功能,但它们的发送可能会打断和分散用户的注意力。以前的工作研究了干预措施,如将通知发送推迟到适当的时刻,但没有系统地研究用户可能更喜欢使用智能系统来管理他们的通知。因此,我们通过为期一周的体验抽样研究(N=35),直接调查了Android智能手机用户的通知偏好。我们发现,用户更喜欢通过抑制警报而不是推迟警报来缓解不想要的干扰,并且更频繁地参考通知内容因素而不是上下文因素来解释他们的偏好。然后,我们展示了利用用户操作来帮助预测通知首选项的挑战和潜力。具体地说,我们展示了使用用户操作进行个性化的模型比通用模型获得了39%的性能提升。这一改进类似于使用从用户请求的标签获得的42%的性能提升,而使用可观察到的用户操作不会导致额外的中断。
Notifications are an indispensable feature of mobile devices, but their delivery can interrupt and distract users. Prior work has examined interventions, such as deferring notification delivery to opportune moments, but has not systematically studied how users might prefer an intelligent system to manage their notifications. Hence, we directly probed Android smartphone users’ notification preferences via a one-week experience-sampling study (N = 35). We found that users prefer mitigating undesired interruptions by suppressing alerts over deferring them and referred to notification content factors more frequently than contextual factors for explaining their preferences. Then we demonstrated the challenges and potentials of leveraging user actions to help predict notification preferences. Specifically, we showed that a model personalized using user actions achieved a performance gain of 39% than a generic model. This improvement is similar to the 42% performance gain using labels solicited from the user while using observable user actions causes no extra disruption.
DOI: --
发表时间: 2006-08
期刊: --
影响因子: --
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发表时间: 2018
期刊: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
影响因子: --
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