LDA for on-the-fly auto tagging

Authors:
Ernesto Diaz-Aviles;Mihai Georgescu;Avaré Stewart;Wolfgang Nejdl
Affiliations:
Leibniz Universität Hannover, Hannover, Germany;Leibniz Universität Hannover, Hannover, Germany;Leibniz Universität Hannover, Hannover, Germany;Leibniz Universität Hannover, Hannover, Germany
Venue:
Proceedings of the fourth ACM conference on Recommender systems
Year:
2010

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Abstract

In this paper, we propose a method for automatic tagging sparse and short textual resources. In the presence of a new resource, our method creates an ad hoc corpus of related resources, then applies Latent Dirichlet Allocation (LDA) to elicit latent topics for the resource and the associated corpus. This is done in order to automatically tag the resource based on the most likely tags derived from the latent topics identified. We evaluate our method, using an offline analysis on publicly available BibSonomy dataset and an online study, showing its effectiveness.