Song Clustering Using Peer-to-Peer Co-occurrences

Peer-to-peer (p2p) content sharing networks are commonly used by millions of users for sharing music files, often performed by artists even before becoming mainstream. In such networks, as well as modern Web 2.0 services, users with similar musical taste often share similar files. This results in songs that have similar properties to be shared together by many users, where the higher the number of song co-occurrences in different users, the stronger is the indication of a tight relationship between these songs. In this work we leverage this feature and propose methods for detecting these "natural" clusters of similar songs. The resulting clusters are shown to be useful in recommender systems, as they almost mitigate the need to use meta-data which is known to be noisy due to its user-generated nature. We present data collected from the Gnutella network and its properties and show two techniques for recommending content to users, one is based on clustering similar-minded users and the other creates song similarity graph and maps users to clusters based on their songs. We show that both techniques result in relatively accurate recommendations, indicating that p2p networks can be leveraged for creating useful recommender systems that can be used for easier content retrieval.