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To provide intelligent personalized online services such as web recommender systems, it is usually necessary to model users' web access behavior. To achieve this, one of the promising approaches is web usage mining, which mines web logs for user models and recommendations. Web usage mining algorithms have been widely utilized for modeling user web navigation behavior. In this paper, the authors advance a model for mining of user's navigation pattern. The model is based on Expectation-Maximization (EM) algorithm and it is used for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables.
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