AppNow: Predicting Usages of Mobile Applications on Smart Phones

Author

Zhung-Xun Liao ; Po-Ruey Lei ; Tsu-Jou Shen ; Shou-Chung Li ; Wen-Chih Peng

Author_Institution

Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan

fYear

2012

fDate

16-18 Nov. 2012

Firstpage

300

Lastpage

303

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.

Keywords

data mining; graphical user interfaces; mobile computing; smart phones; Apps browsing; Apps execution; mobile application usage prediction; smart phone; temporal profile; touch screen; Accuracy; Computer science; Educational institutions; History; Pervasive computing; Probability; Smart phones; data mining; mobile application; prediction; temporal profile;

fLanguage

English

Publisher

ieee

Conference_Titel

Technologies and Applications of Artificial Intelligence (TAAI), 2012 Conference on

Conference_Location

Tainan

Print_ISBN

978-1-4673-4976-5

Type

conf

DOI

10.1109/TAAI.2012.18

Filename

6395044