DocumentCode :
2728018
Title :
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
Link To Document :
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