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Title:
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IMPROVING TAG RECOMMENDATIONS IN SOCIAL BOOKMARKING SYSTEMS: A PRELIMINARY STUDY |
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Author(s):
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Domenico Gendarmi , Filippo Lanubile |
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ISBN:
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978-972-8924-93-5 |
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Editors:
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Pedro Isaías, Bebo White and Miguel Baptista Nunes |
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Year:
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2009 |
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Edition:
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1 |
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Keywords:
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Tag recommender, collaborative tagging, suggestive tagging, evaluation |
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Type:
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Full Paper |
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First Page:
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133 |
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Last Page:
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140 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Embedded tagging functionality in Web 2.0 applications allows users to organize arbitrary types of digital resources.
Current suggestive tagging features recommend tags through tag clouds that emphasizes tags just on the basis of their
popularity. This paper presents a tag recommender for a social bookmarking system. Tag recommendations are computed
according to a semantic content analysis of the digital resource and exploiting the personal tagging history of users as
well as the social tagging process within the system. A first evaluation against a snapshot of the BibSonomy dataset
reveals that, in particular conditions, the combination of these three different aspects of a social bookmarking system can
improve the precision of generated tag suggestions. |
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