How Busy Are You?: Predicting the Interruptibility Intensity of Mobile Users

論文URL:http://dl.acm.org/citation.cfm?doid=3025453.3025946

論文アブストラクト:Smartphones frequently notify users about newly available messages or other notifications. It can be very disruptive when these notifications interrupt users while they are busy. Our work here is based on the observation that people usually exhibit different levels of busyness at different contexts. This means that classifying users' interruptibility as a binary status, interruptible or not interruptible, is not sufficient to accurately measure their availability towards smartphone interruptions. In this paper, we propose, implement and evaluate a two-stage hierarchical model to predict people's interruptibility intensity. Our work is the first to introduce personality traits into interruptibility prediction model, and we found that personality data improves the prediction significantly. Our model bootstraps the prediction with similar people's data, and provides a good initial prediction for users whose individual models have not been trained on their own data yet. Overall prediction accuracy of our model can reach 66.1%.

日本語のまとめ:

2段階の階層モデルを提案、実装、評価し、人々の割り込み可能性の強さを予想する研究。性格特性を中断予想モデルに導入された提案モデルは、まだ学習を行なっていない初期予想の重要な問題を解決することができる。

(100文字)

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