User Intention Modeling in Web Applications Using Data Mining

Zheng Chen, Fan Lin, Huan Liu, Yin Liu, Wei Ying Ma, Liu Wenyin

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)181-191
Number of pages11
JournalWorld Wide Web
Issue number3
StatePublished - Sep 2002


  • Web navigation
  • data mining
  • intention modeling
  • machine learning
  • user modeling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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