Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 13, Issue 7, Pages 1550-1563Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2013.113
Keywords
Mobile App classification; web knowledge; real-world contexts; enriched contextual information
Funding
- National Science Foundation for Distinguished Young Scholars of China [61325010]
- Natural Science Foundation of China (NSFC) [71329201]
- National High Technology Research and Development Program of China [SS2014AA012303]
- Science and Technology Development of Anhui Province [1301022064]
- International Science and Technology Cooperation Plan of Anhui Province [1303063008]
- US National Science Foundation (NSF) [CCF-1018151, IIS-1256016]
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The study of the use of mobile Apps plays an important role in understanding the user preferences, and thus provides the opportunities for intelligent personalized context-based services. A key step for the mobile App usage analysis is to classify Apps into some predefined categories. However, it is a nontrivial task to effectively classify mobile Apps due to the limited contextual information available for the analysis. For instance, there is limited contextual information about mobile Apps in their names. However, this contextual information is usually incomplete and ambiguous. To this end, in this paper, we propose an approach for first enriching the 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, we also extract some contextual features for mobile Apps from the context-rich device logs of mobile users. Finally, we combine all the enriched contextual information into the Maximum Entropy model for training a mobile App classifier. To validate the proposed method, we conduct extensive experiments on 443 mobile users' device logs to show both the effectiveness and efficiency of the proposed approach. The experimental results clearly show that our approach outperforms two state-of-the-art benchmark methods with a significant margin.
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