Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models
Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models
复制标题
使用决策理论经验采样构建个性化手机中断模型
DOI：
10.1007/978-3-642-21726-5_11
复制
发表时间：
2011
影响因子：
--
通讯作者：
M. Veloso
中科院分区：
文献类型：
--
作者：
Stephanie Rosenthal;A. Dey;M. Veloso
我们贡献了一种方法，用于近似用户的可中断性成本用于经验采样和验证的方法在应用程序中，学习何时自动关闭和打开电话音量，以避免尴尬的电话中断。我们证明，用户有不同的成本与中断，这表明需要个性化的成本近似。我们比较了不同的经验采样技术，以了解用户的音量偏好，并显示那些询问我们的成本近似值较低时，减少了令人尴尬的中断的数量，并导致更准确的音量分类器时，部署为长期使用。
We contribute a method for approximating users' interruptibility costs to use for experience sampling and validate the method in an application that learns when to automatically turn off and on the phone volume to avoid embarrassing phone interruptions. We demonstrate that users have varying costs associated with interruptions which indicates the need for personalized cost approximations. We compare different experience sampling techniques to learn users' volume preferences and show those that ask when our cost approximation is low reduce the number of embarrassing interruptions and result in more accurate volume classifiers when deployed for long-term use.