document: A Web page prediction model based on click-stream tree representation of user behavior Predicting the next request of a user as she visits Web pages has gained importance as Web-based activity increases . Markov models and their variations , or models based on sequence mining have been found well suited for this problem . However , higher order Markov models are extremely complicated due to their large number of states whereas lower order Markov models do not capture the entire behavior of a user in a session . The models that are based on sequential pattern mining only consider the frequent sequences in the data set , making it difficult to predict the next request following a page that is not in the sequential pattern . Furthermore , it is hard to find models for mining two different kinds of information of a user session . We propose a new model that considers both the order information of pages in a session and the time spent on them . We cluster user sessions based on their pair-wise similarity and represent the resulting clusters by a click-stream tree . The new user session is then assigned to a cluster based on a similarity measure . The click-stream tree of that cluster is used to generate the recommendation set . The model can be used as part of a cache prefetching system as well as a recommendation model .
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