User interest in topics and resources is known to be recurrent and to follow specific patterns, depending on the type of topic or resource. Traditional methods for predicting reoccurring patterns are based on ranking and associative models. In this paper, we identify several 'canonical' patterns by clustering keywords related to visited resources, making use of a large repository of Web usage data. The keywords are derived from a 'virtual' folksonomy of tags assigned to these resources, using a collaborative bookmarking system.
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