People's Interruptibility in-the-wild: Analysis of Breakpoint Detection Model in a Large-Scale Study
Kota Tsubouchi, Tadashi Okoshi（Keio University）
UbitTention 2017, 2017/9
機械学習 (Machine Learning) データサイエンス (Data Science) 次世代UI・インタラクション (HCI)
- In the advancing ubiquitous computing where users have been having an increasing amount of push-style information provision from lots of intelligent proactive services, detecting the users' current attentional status and/or interruptibility has been a significant issue. In our previous study on interruptibility detection in "Yahoo! JAPAN" real world product for 21 days with more than 680,000 users, the result showed significant lower user response time in exchange for notification delivery delay for about 4 minutes on average. In this paper, we further analyze the features in the interruptibility detection model trained and updated during this study in nightly basis for 21 days and report the results.
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