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

• Published in: ACM transactions on computer-human interaction 2023-01, Vol.29 (5), p.1-33, Article 39
• Main Authors: Li, Tianshi, Haines, Julia Katherine, De Eguino, Miguel Flores Ruiz, Hong, Jason I., Nichols, Jeffrey
• Format: Article
• Language: English
• Subjects: Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing; Human-centered computing
• ISSN: 1073-0516; 1557-7325
• DOI: 10.1145/3478868

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.