Cyberguide: a mobile context-aware tour guide
Wireless Networks - Special issue: mobile computing and networking: selected papers from MobiCom '96
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Context-Aware Query Refinement for Mobile Web Search
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Who, what, where & when: a new approach to mobile search
Proceedings of the 13th international conference on Intelligent user interfaces
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
An Unsupervised Approach to Modeling Personalized Contexts of Mobile Users
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Mobile search market is growing very fast. Mining mobile search activities is helpful for understanding user preference, interest and even regular patterns. In previous works, text information contained by either search queries or web pages visited by users is well studied to mine search activities. Since rich context information (e.g., time, location and other sensor inputs) is contained in the mobile search data, it has also been leveraged by researchers for mining user activities. However, the two types of information were used separately. In this paper, we propose a graphical model approach, namely the Text and Context-based User Activity Model (TCUAM), which mines user activity patterns by utilizing query text and context simultaneously. The model is developed based on Latent Dirichlet Allocation (LDA) by regarding users' activities as latent topics. In order to guide the activity mining process, we borrow some external knowledge of topic-word relationship to build a constrained TCUAM model. The experimental results indicate that the TCUAM model yields better results compared with text-only and context-only approaches. We also find that the constrained TCUAM model is more effective than the unconstrained TCUAM model.