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RichVSM: enRiched vector space models for folksonomies

People share millions of resources (photos, bookmarks, videos, etc.) in Folksonomies (like Flickr, Delicious, Youtube, etc.). To access and share resources, they add keywords called tags to the resources. As the tags are freely chosen keywords, it might not be possible for users to tag their resources with all the relevant tags. As a result, many resources lack sufficient number of relevant tags. The lack of relevant tags results into sparseness of data, and this sparseness of data makes many relevant resources unsearchable against user queries.
 In this paper, we explore two dimensions of semantic relationships between tags, based on the context and the distribution of tags. We exploit semantic relationships between tags to reduce sparseness in Folksonomies and propose different enriched vector space models. We also propose a vector space model Best of Breed which utilizes appropriate enrichment method based on the type of the query. We evaluate the proposed methods on a large dataset of 27 million resources, 92 thousand tags and 94 million tag assignments. Experimental results show that the enriched vector space models help in improving search, especially for the rare queries which have few relevant resources in the sparse data.