User Intention Modeling in Web Applications Using Data Mining

  • Authors:

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

  • Affiliations:

  • Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, PR China zhengc@microsoft.com;Department of Computer of Science and Technology, Tsinghua University, Beijing 100084, PR China lfan@acm.org;Arizona State University, PO Box 875406 Tempe, AZ 85287-5406, USA hliu@asu.edu;Department of Computer Science and Engineering, Tongji University, Shanghai, PR China liu_yin_@hotmail.com;Microsoft Research Asia, 49 Zhichun Road, Beijing 100080, PR China wyma@microsoft.com;Department of Computer Science, City University of Hong Kong, Hong Kong SAR, PR China csliuwy@cityu.edu.hk

  • Venue:
  • World Wide Web
  • Year:
  • 2002

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Abstract

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.