Tag Recommendation using Probabilistic Topic Models
ECML PKDD Discovery Challenge 2009 (DC09), 497, page 131--141. Bled, Slovenia, CEUR Workshop Proceedings, (September 2009)Abstract
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. Resources annotated by many users and thus equipped with a fairly stable and complete tag set are used to elicit latent topics represented as a mixture of description tokens and tags. Based on this, new resources are mapped to latent topics based on their content in order to recommend the most likely tags from the latent topics. We evaluate recall and precision for the bibsonomy benchmark provided within the ECML PKDD Discovery Challenge 2009.
