@inproceedings{4f78d4afb886454bb3d9a542b8fdf278,
title = "A preliminary attempt of an intelligent system predicting users' correctness of notifications' sender speculation",
abstract = "Prior interruptibility research has focused on identifying interruptible or opportune moments for users to handle notifications. Yet, users may not want to attend to all notifications even at these moments. Research has shown that users' current practices for selective attendance are through speculating about notification sources. Yet, sometimes the above information is insufficient, making speculations difficult. This paper describes the first research attempt to examine how well a machine learning model can predict the moments when users would incorrectly speculate the sender of a notification. We built a machine learning model that can achieve an recall: 84.39%, precision: 56.78%, and F1-score of 0.68. We also show that important features for predicting these moments.",
keywords = "intelligent system, notification source, receptivity",
author = "Chang, {Tang Jie} and Chen, {Jian Hua Jiang} and Lee, {Hao Ping} and Chang, {Yung Ju}",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020 ; Conference date: 12-09-2020 Through 17-09-2020",
year = "2020",
month = sep,
day = "10",
doi = "10.1145/3410530.3414390",
language = "English",
series = "UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers",
publisher = "Association for Computing Machinery",
pages = "13--16",
booktitle = "UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers",
}