Collaborative resource discovery in social tagging systems.

Social tagging systems which allow users to create, edit and share collections of internet resources associated with tags in a collaborative fashion are growing in popularity in recent years. The rapidly growing amount of shared data in these folksonomies, i.e., taxonomies created by the folk, presents new technical challenges involved with discovering resources which are likely of interest to the user. Social tags which reflect the meaning of resources from the user's points of view provide an opportunity to enhance the quality of retrieval. In this paper, we introduce a novel framework to search relevant resources to the user query by incorporating information obtained from folksonomies' underlying data structures consisting of a set of user/tag/resource triplets. In contrast to traditional retrieval and recommendation techniques which represent a collection by a matrix, we represent our data as a third-order tensor on which a novel Cube Latent Semantic Indexing (CubeLSI) technique is proposed to capture latent semantic associations between tags. With the latent semantic representation we show how to rank relevant resources according to their relevance to user queries. The excellent performance of the method is demonstrated by an experimental evaluation on the deli.cio.us dataset.