Towards expert finding by leveraging relevant categories in authority ranking
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding experts in tag based knowledge sharing communities
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
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
Mining mobile users' activities based on search query text and context
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Context-Aware Expert Finding in Tag Based Knowledge Sharing Communities
International Journal of Knowledge and Systems Science
A probabilistic approach to mining mobile phone data sequences
Personal and Ubiquitous Computing
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Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which is critical for the success of context-aware mobile services. While there are prior work on mobile context modeling, the use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques have the ability to learn personalized contexts which are difficult to be predefined. To that end, in this paper, we propose an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit topic models to learn personalized contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users.