3 Amélie Anglade Marco Tiemann Fabio Vignoli 2007 Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007) Vienna, Austria 95-100 September, 2007 We present an approach to automatically create virtual communities of users with similar music tastes. Our goal is to create personalized music channels for these communities in a distributed way, so that they can for example be used in peer-to-peer networks. To find suitable techniques for creating these communities we analyze graphs created from real-world recommender datasets and identify specific properties of these datasets. Based on these properties we select and evaluate different graph-based community-extraction techniques. We select a technique that exploits identified properties to create clusters of music listeners. We validate the suitability of this technique using a music dataset and a large movie dataset. On a graph of 6,040 peers, the selected technique assigns at least 85% of the peers to optimal communities, and obtains a mean classification error of less than 0.05 over the remaining peers that are not assigned to the best community.