Classification of user interest patterns using a virtual folksonomy
Ricardo Kawase, Eelco Herder · 2011
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.