Abstract
Mobile Application activates an important role in the daily lives of mobile users. Intuitively, the study of the use of mobile Apps can help to understand the user favorites, such as App recommendation, user segmentation and target advertising. The major challenge is that there are not many effective and explicit features available for classification models due to the limited contextual information of Apps available for the analysis. From the CDMA to recent mobile applications more user activities have improved. Still now all mobile app have limited contextual information in their names, and the only available explicit features of mobile Apps are the semantic words contained in their names and proposing enriched contextual information of mobile Apps by exploiting the additional Web knowledge from the Web search engine. Then, inspired by the observation that different types of mobile Apps may be relevant to different real-world contexts, also extract some contextual features for mobile Apps from the context-rich device logs of mobile users. By collecting details about the user side information regarding the app usage to categorizing the mobile application on own. Specifically natural language processing has been driven for the classification work. Finally combining all the enriched contextual information into the classified framework model for training the mobile App classifier.
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