Tagging with DHARMA, a DHT-based Approach for Resource Mapping through Approximation

TL;DR

This paper presents DHARMA, a DHT-based resource mapping approach for collaborative tagging and faceted search in P2P systems, using approximation techniques to improve scalability and reduce noise.

Contribution

It introduces an approximation method for folksonomy mapping on DHT systems, enhancing scalability and reducing hotspots and noise.

Findings

Reduces vocabulary noise and overfitting.

Improves scalability of folksonomy mapping.

Proven effective on Last.fm data.

Abstract

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.