2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI) (2012)
Nov. 16, 2012 to Nov. 18, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAAI.2012.18
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.
data mining, graphical user interfaces, mobile computing, smart phones
Z. Liao, P. Lei, T. Shen, S. Li and W. Peng, "AppNow: Predicting Usages of Mobile Applications on Smart Phones," 2012 Conference on Technologies and Applications of Artificial Intelligence(TAAI), Tainan, Taiwan Taiwan, 2013, pp. 300-303.