2011-10-01T00:34:59Z2011-10-01T00:34:59Zhttp://cogprints.org/id/eprint/7646This item is in the repository with the URL: http://cogprints.org/id/eprint/76462011-10-01T00:34:59ZMeasuring Similarity in Large-Scale FolksonomiesSocial (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.Giovanni QuattroneEmilio FerraraPasquale De MeoLicia Capra