Latent Dirichlet Allocation for Tag Recommendation

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Added by Martin on 2009-12-06 14:26

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Creator(s): Ralf Krestel, Peter Fankhauser, Wolfgang Nejdl

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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.

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Latent Dirichlet Allocation for Tag Recommendation 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. Ralf Krestel, Peter Fankhauser, Wolfgang Nejdl