Mobile Apps Prediction by Environmental and InteractionalContexts

• Main Authors: Ya-Ting Hu, 胡雅婷
• Other Authors: 陳銘憲
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/71386841819451518125

碩士 === 國立臺灣大學 === 電機工程學研究所 === 102 === With the growing prevalence of smartphones, there is an increasing number of mobile applications which play important roles in daily life. In this thesis, we propose a framework of APEIC (standing for App Prediction by Environmental and Interactional Contexts) to predict apps that are most likely to be used according to the current context. The context consists of environmental context (EC), which is characterized by features extracted from built-in sensors, and interactional context (IC), which is defined as the app launch sequence. The benefits of such prediction include fast app launching by pre-loading the right apps into memory, and also efficient power management by terminating apps which are not to be used in the near future. We collected real app usage traces and made some observations that provide insights into the design of our prediction model. First, from past traces, we adopt features representing EC to build a naive Bayes classifier and evaluate the launch contributions between apps from IC respectively. Second, from the current condition, Poisson distribution is used to model the re-access pattern of certain apps. Finally, we can rank the apps by the sum of their launch intensities. We conduct experiments on both real data and synthetic data. The results demonstrate the capability and the robustness of our prediction framework. Furthermore, in combination with our companions’ work, we design a smart launcher which helps users have rapid access to the apps they need and recommends other useful apps according to the context at that time.