Measuring Similarity in Large-scale Folksonomies
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike 
taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best 
describe some content. However, as tags are informally de- 
ﬁned, continually changing, and ungoverned, social tagging 
has often been criticised for lowering, rather than increasing, the efﬁciency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of 
users and the noise they introduce. To address this issue, a 
variety of approaches have been proposed that recommend 
users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world 
folksonomies are characterized by power law distributions 
of tags, over which commonly used similarity metrics, including the Jaccard coefﬁcient and the cosine similarity, fail 
to compute. We thus propose a novel metric, speciﬁcally 
developed to capture similarity in large-scale folksonomies, 
that is based on a mutual reinforcement principle: that is, 
two tags are deemed similar if they have been associated to 
similar resources, and vice-versa two resources are deemed 
similar if they have been labelled by similar tags. We offer an efﬁcient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike.