Title: Guiding user groupings
Other Titles: Learning and combining classiification for itemset structuring
Authors: Verbeke, Mathias ×
Subasic, Ilija
Berendt, Bettina #
Issue Date: 2012
Host Document: Proceedings of the 3rd International Workshop on Mining Ubiquitous and Social Environments (MUSE)
Conference: Mining Ubiquitous and Social Environments (MUSE) edition:3 location:Bristol, UK date:24 September 2012
Abstract: Structuring is one of the fundamental activities needed to understand data. Human structuring activity lies behind many of the datasets found on the Internet that contain grouped instances, such as file or email folders, tags and bookmarks, ontologies and linked data. Understanding the dynamics of large-scale structuring activities is a key prerequisite for theories of individual behaviour in collaborative settings as well as for applications such as recommender systems. In particular, a key question is to what extent the "structurer" - be it human or machine - is driven by his/its own prior structures, and to what extent by the structures created by others such as one's communities.

In this paper, we propose a methodology for identifying these dynamics. The methodology relies on dynamic conceptual clustering, and it simulates an intellectual structuring process operating over an extended period of time. The development of a grouping of dynamically changing items follows a dynamically changing and collectively determined "guiding grouping". The analysis of a real-life dataset of a platform for literature management suggests that even in such a typical "Web 2.0" environment, users are guided somewhat more by their own previous behaviour than by their peers.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
MUSE_2012_CameraReadyLAST.pdf Published 508KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.