An unsupervised approach to modeling personalized contexts of mobile users

Tengfei Bao, Huanhuan Cao, Enhong Chen, Jilei Tian, Hui Xiong

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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 works 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 two methods for mining personalized contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual feature-value pairs from context session clusters as contexts. The second method leverages 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.

Original languageEnglish (US)
Pages (from-to)345-370
Number of pages26
JournalKnowledge and Information Systems
Issue number2
StatePublished - May 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


  • Mobile context modeling
  • Unsupervised approach


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