Degree Type

Thesis

Date of Award

2008

Degree Name

Master of Science

Department

Computer Science

First Advisor

Vasant Honavar

Second Advisor

David-Fernandez Baca

Third Advisor

Samik Basu

Abstract

Folksonomies - shared vocabularies generated by users through collective annotation (tagging) of web-based content, which are formally hypergraphs connecting users, tags and objects, are beginning to play an increasingly important role in social media. Effective use of folksonomies for organizing and locating web content, discovering and organizing user communities in order to facilitate the contact and collaboration between users who share parts of their interests and attitudes calls for effective methods for discovering coherent groupings of users, objects, and tags. We empirically compare the results of several folksonomy clustering methods using tensor decompositions such as PARAFAC, Tucker3 and HOSVD which are generalizations of principal component analysis and singular value decomposition with standard methods that use 2-dimensional projections of the original 3-way relationships. Our results suggest that the proposed methods overcome some of the limitations of 2-way decomposition methods in clustering folksonomies.

DOI

https://doi.org/10.31274/rtd-180813-16063

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Flavian Vasile

Language

en

Proquest ID

AAI1453052

OCLC Number

236168394

ISBN

9780549540700

File Format

application/pdf

File Size

70 pages

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