Estimating peer similarity using distance of shared files
IPTPS'10 Proceedings of the 9th international conference on Peer-to-peer systems
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
What is a "Musical World"? An affinity propagation approach
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Quantifying paedophile activity in a large P2P system
Information Processing and Management: an International Journal
Personalization in multimodal music retrieval
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
An approach to automatic music band member detection based on supervised learning
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
| Hi-index | 0.01 |
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.