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Content-awareness and graph-based ranking for tag recommendation in folksonomies
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Landia, Nikolas (2013) Content-awareness and graph-based ranking for tag recommendation in folksonomies. PhD thesis, University of Warwick.
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WRAP_THESIS_Landia_2013.pdf - Submitted Version Download (1809Kb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b2691231~S1
Abstract
Tag recommendation algorithms aid the social tagging process in many userdriven
document indexing applications, such as social bookmarking and publication
sharing websites. This thesis gives an overview of existing tag recommendation
methods and proposes novel approaches that address the new document problem
and the task of ranking tags. The focus is on graph-based methods such as Folk-
Rank that apply weight spreading algorithms to a graph representation of the folksonomy.
In order to suggest tags for previously untagged documents, extensions are
presented that introduce content into the recommendation process as an additional
information source. To address the problem of ranking tags, an in-depth analysis
of graph models as well as ranking algorithms is conducted. Implicit assumptions
made by the widely-used graph model of the folksonomy are highlighted and an
improved model is proposed that captures the characteristics of the social tagging
data more accurately. Additionally, issues in the tag rank computation of FolkRank
are analysed and an adapted weight spreading approach for social tagging data is
presented. Moreover, the applicability of conventional weight spreading methods to
data from the social tagging domain is examined in detail. Finally, indications of
implicit negative feedback in the data structure of folksonomies are analysed and
novel approaches of identifying negative relationships are presented. By exploiting
the three-dimensional characteristics of social tagging data the proposed metrics are
based on stronger evidence and provide reliable measures of negative feedback.
Including content into the tag recommendation process leads to a significant
increase in recommendation accuracy on real-world datasets. The proposed adaptations
to graph models and ranking algorithms result in more accurate and computationally
less expensive recommenders. Moreover, new insights into the fundamental
characteristics of social tagging data are revealed and a novel data interpretation
that takes negative feedback into account is proposed.
Item Type: | Thesis (PhD) | ||||
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Subjects: | Q Science > QA Mathematics Z Bibliography. Library Science. Information Resources > ZA Information resources |
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Library of Congress Subject Headings (LCSH): | User-generated content, Metadata, Web sites -- Abstracting and indexing, Web sites -- Abstracting and indexing -- Graphic methods, Algorithms | ||||
Official Date: | April 2013 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Department of Computer Science | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Anand, Sarabjot Singh; Griffiths, Nathan | ||||
Extent: | ix, 123 leaves : illustrations. | ||||
Language: | eng |
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