RECSYS 09
Latent Dirichlet Allocation for Tag Recommendation
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 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 precision
and recall than the use of association rules, suggested in previous work, and
also recommends more specific tags. Moreover, extending resources with these
recommended tags significantly improves search for new resources.
Full Paper
RECSYS09.pdf
Conference Homepage
RecSys 2009
BibTex Entry
