CalBehav：基于智能手机时间序列数据的基于机器学习的个性化日历行为模型
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data
摘要：
电子日历是当今管理我们日常生活约会或日程安排的宝贵资源，也称为事件，范围从专业到高度个人化。研究人员研究了各种类型的日历事件，以预测智能手机用户对传入移动通信的行为。然而，这些研究通常没有考虑到个体之间的行为差异。在现实世界中，智能手机用户在他们预定的事件期间如何响应传入通信方面可能彼此差异很大。此外，个体用户可以在不同的上下文中不同地响应传入通信，这取决于在她的个人日历中安排了什么类型的事件。因此，用于个体智能手机用户的基于静态手机的行为模型不一定反映他们对传入通信的行为。在本文中，我们提出了一个基于机器学习的上下文感知模型，该模型是个性化的，并使用记录的时间序列智能手机数据动态识别个人的主要行为，并简称为"CalBehav'"。基于日历和电话日志的真实数据集的实验结果表明，与现有的基于日历的方法相比，这种数据驱动的个性化模型更有效地智能管理传入的移动通信。
英文摘要：
The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
