標題: Mining Temporal Profiles of Mobile Applications for Usage Prediction
作者: Liao, Zhung-Xun
Lei, Po-Ruey
Shen, Tsu-Jou
Li, Shou-Chung
Peng, Wen-Chih
資訊工程學系
Department of Computer Science
關鍵字: mobile application;temporal profile;prediction;data mining
公開日期: 2012
摘要: 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 Apps by 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. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. 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.
URI: http://hdl.handle.net/11536/21978
http://dx.doi.org/10.1109/ICDMW.2012.11
ISBN: 978-0-7695-4925-5
DOI: 10.1109/ICDMW.2012.11
期刊: 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012)
起始頁: 890
結束頁: 893
Appears in Collections:Conferences Paper


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