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In & out zooming on time-aware user/tag clusters.

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Title: In & out zooming on time-aware user/tag clusters.
Authors: Giannakidou, Eirini1 eirgiann@csd.auth.gr, Koutsonikola, Vassiliki1, Vakali, Athena1, Kompatsiaris, Ioannis2
Source: Journal of Intelligent Information Systems. Jun2012, Vol. 38 Issue 3, p685-708. 24p.
Subjects: Tags (Metadata), Metadata mapping, Temporal databases, Visualization, Computer users
Abstract: The common ground behind most approaches that analyze social tagging systems is addressing the information challenge that emerges from the massive activity 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 denies much of the dynamic nature of social tagging activity. In this paper we claim that holding a temporal dimension, allows for tracking macroscopic and microscopic users' interests, detecting emerging trends and recognizing events. To this end, we propose a time-aware co-clustering approach for acquiring semantic and temporal patterns out of the tagging activity. The resulted clusters contain both users and tags of similar patterns over time, and reveal non-obvious or 'hidden' relations among users and topics of their common interest. Zoom in & out views serve as visualization methods on different aspects of the clusters' structure, in order to evaluate the efficiency of the approach. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: The common ground behind most approaches that analyze social tagging systems is addressing the information challenge that emerges from the massive activity 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 denies much of the dynamic nature of social tagging activity. In this paper we claim that holding a temporal dimension, allows for tracking macroscopic and microscopic users' interests, detecting emerging trends and recognizing events. To this end, we propose a time-aware co-clustering approach for acquiring semantic and temporal patterns out of the tagging activity. The resulted clusters contain both users and tags of similar patterns over time, and reveal non-obvious or 'hidden' relations among users and topics of their common interest. Zoom in & out views serve as visualization methods on different aspects of the clusters' structure, in order to evaluate the efficiency of the approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2012
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