A vector space model for automatic indexing
Communications of the ACM
The Journal of Machine Learning Research
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
An effective approach for mining mobile user habits
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Ranking in context-aware recommender systems
Proceedings of the 20th international conference companion on World wide web
A Hidden Topic-Based Framework toward Building Applications with Short Web Documents
IEEE Transactions on Knowledge and Data Engineering
Towards expert finding by leveraging relevant categories in authority ranking
Proceedings of the 20th ACM international conference on Information and knowledge management
Personalized Travel Package Recommendation
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
A habit mining approach for discovering similar mobile users
Proceedings of the 21st international conference on World Wide Web
Towards personalized context-aware recommendation by mining context logs through topic models
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
BP-growth: Searching Strategies for Efficient Behavior Pattern Mining
MDM '12 Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)
Ranking fraud detection for mobile apps: a holistic view
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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. To this end, in this paper, we propose an approach to first enrich 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 a Maximum Entropy model for training a mobile app classifier. The experimental results based on 443 mobile users' device logs clearly show that our approach outperforms two state-of-the-art benchmark methods with a significant margin.