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Journal of Intelligent Information Systems

Erschienen in:

01.04.2013

Folksonomy link prediction based on a tripartite graph for tag recommendation

verfasst von: Majdi Rawashdeh, Heung-Nam Kim, Jihad Mohamad Alja’am, Abdulmotaleb El Saddik

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2013

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Abstract

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.

Vorheriger Artikel Iterative classification for multiple target attributes

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http://citeulike.org

http://www.last.fm/

http://www.citeulike.org/faq/data.adp

http://www.grouplens.org/node/462

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Titel: Folksonomy link prediction based on a tripartite graph for tag recommendation
verfasst von: Majdi Rawashdeh
Heung-Nam Kim
Jihad Mohamad Alja’am
Abdulmotaleb El Saddik
Publikationsdatum: 01.04.2013
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2013
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-012-0227-2

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