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User Intention Modeling in Web Applications Using Data Mining

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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.

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Chen, Z., Lin, F., Liu, H. et al. User Intention Modeling in Web Applications Using Data Mining. World Wide Web 5, 181–191 (2002). https://doi.org/10.1023/A:1020980528899

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  • DOI: https://doi.org/10.1023/A:1020980528899

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