@article {1828,
	title = {In \& out zooming on time-aware user/tag clusters},
	year = {2011},
	abstract = {<p>The common ground behind most approaches that analyze social taggingsystems is addressing the information challenge that emerges from the massiveactivity of millions of users who interact and share resources and/or metadata online.However, lack of any time-related data in the analysis process implicitly deniesmuch of the dynamic nature of social tagging activity. In this paper we claim thatholding a temporal dimension, allows for tracking macroscopic and microscopicusers{\textquoteright} interests, detecting emerging trends and recognizing events. To this end, wepropose a time-aware co-clustering approach for acquiring semantic and temporalpatterns out of the tagging activity. The resulted clusters contain both users and tagsof similar patterns over time, and reveal non-obvious or {\textquotedblleft}hidden{\textquotedblright} relations amongusers and topics of their common interest. Zoom in \&amp; out views serve as visualizationmethods on different aspects of the clusters{\textquoteright} structure, in order to evaluate theefficiency of the approach.</p>
}
}