AppNow: Predicting Usages of Mobile Applications on Smart Phones

Zhung-Xun Liao, Po-Ruey Lei, Tsu-Jou Shen, Shou-Chung Li, Wen-Chih Peng
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引用次数: 6

Abstract

Due to the proliferation of mobile applications(abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Appsby swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.
AppNow:预测智能手机上移动应用的使用情况
由于智能手机上的移动应用程序(简称Apps)的激增,用户可以安装许多应用程序来方便他们的生活。通常,用户通过滑动智能手机的触摸屏来浏览应用程序,并且可能会花很多时间浏览应用程序。在本文中,我们设计了一个能够预测用户应用使用情况的AppNow小部件。因此,用户可以简单地从小部件执行Apps。本文的主要主题是构建识别应用程序与其使用时间之间关系的时间概况。根据应用程序的时间配置文件,AppNow小部件预测当前最有可能使用的应用程序列表。在我们的实验中,我们收集了真实的使用轨迹,结果表明AppNow在识别时间概况方面的准确率可以达到86%,在预测应用使用情况方面的准确率可以达到90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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