A FRAMEWORK FOR EXTRACTING MUSICAL SIMILARITIES FROM PEER-TO-PEER NETWORKS

The usage of peer-to-peer (p2p) networks for music information retrieval (MIR) tasks is gaining momentum. P2P file sharing networks can be used for collecting both search queries and files from shared folders. The first can be utilized to reveal current taste, users interest, and trends, while the latter can be used for enhancing recommender systems. Both provide opportunities for longitudinal analysis, as queries change over time and content often accumulates. Moreover, spatial analysis can expose cultural differences and the way trends propagate. However, tapping into this fountain of information is far from trivial. This paper presents a novel analysis of the shared folders data-set collected from the Gnutella network. We first present the framework for crawling the network and collecting the data. We then present some data-set characteristics, while focusing on music similarities. The paper sheds light on both the opportunities of using p2p data and its complexities.