World Wide Web

, Volume 5, Issue 3, pp 181–191 | Cite as

User Intention Modeling in Web Applications Using Data Mining

  • Zheng Chen
  • Fan Lin
  • Huan Liu
  • Yin Liu
  • Wei-Ying Ma
  • Liu Wenyin


The problem of inferring a user's intentions in Machine–Human Interaction has been the key research issue for providing personalized experiences and services. In this paper, we propose novel approaches on modeling and inferring user's actions in a computer. Two linguistic features – keyword and concept features – are extracted from the semantic context for intention modeling. Concept features are the conceptual generalization of keywords. Association rule mining is used to find the proper concept of corresponding keyword. A modified Naïve Bayes classifier is used in our intention modeling. Experimental results have shown that our proposed approach achieved 84% average accuracy in predicting user's intention, which is close to the precision (92%) of human prediction.

intention modeling user modeling machine learning data mining Web navigation 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Zheng Chen
    • 1
  • Fan Lin
    • 2
  • Huan Liu
    • 3
  • Yin Liu
    • 4
  • Wei-Ying Ma
    • 1
  • Liu Wenyin
    • 5
  1. 1.Microsoft Research AsiaBeijingPR China
  2. 2.Department of Computer of Science and TechnologyTsinghua UniversityBeijingPR China
  3. 3.Arizona State UniversityTempeUSA
  4. 4.Department of Computer Science and EngineeringTongji UniversityShanghaiPR China
  5. 5.Department of Computer ScienceCity University of Hong KongHong Kong SAR, PR China

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