We introduce collaborative tagging and faceted search on structured P2P systems. Since a trivial and brute force mapping of an entire folksonomy over a DHT-based system may reduce scalability, we propose an approximated graph maintenance approach. Evaluations on real data coming from Last.fm prove that such strategies reduce vocabulary noise (i.e., representation's overfitting phenomena) and hotspots issues.
@cite_9: Self-organizing structure and availability of almost unlimited resource capacities make the peer-to-peer architecture very attractive for large-scale sharing of annotated data in Web 2.0 scenarios. This paper addresses the problem of information aggregation and utilization in a decentralized tagging environment. We introduce the vector space model for characterization of users, resources, and tags. We analyze the problem of constructing a reliable approximation for feature vectors in a fully decentralized setting and introduce possible solutions. The results of large-scale systematic evaluation with realistic data sets witness the viability of our approach.
A p2p infrastructure for tagging systems () is proposed in @cite_9 ; in particular, the authors design a scheme to maintain feature vectors for characterization of users and resource of a tagging environment on a DHT. Feature vectors may be useful for calculating the similarity between users or for constructing algorithms for ranked retrieval.