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Latent dirichlet allocation for tag recommendation

10 years 2 months ago
Latent dirichlet allocation for tag recommendation
Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse. Especially new resources annotated by only a few users have often rather idiosyncratic tags that do not reflect a common perspective useful for search. In this paper we introduce an approach based on Latent Dirichlet Allocation (LDA) for recommending tags of resources in order to improve search. Resources annotated by many users and thus equipped with a fairly stable and complete tag set are used to elicit latent topics to which new resources with only a few tags are mapped. Based on this, other tags belonging to a topic can be recommended for the new resource. Our evaluation shows that the approach achieves significantly better precisio...
Ralf Krestel, Peter Fankhauser, Wolfgang Nejdl
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where RECSYS
Authors Ralf Krestel, Peter Fankhauser, Wolfgang Nejdl
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