Publication Type:
Conference PaperSource:
Proceedings of the 1st ACM International Conference on Recommender Systems (RecSys 2007), ACM, Minneapolis, Minnesota, USA, p.41-48 (2007)Abstract:
This article presents an approach to automatically create virtual communities of users with similar music preferences in a distributed system. Our goal is to create personalized music channels for these communities using the content shared by its members in peer-to-peer networks for each community. To extract these communities a complex network theoretic approach is chosen. A fully connected graph of users is created using epidemic protocols. We show that the created graph sufficiently converges to a graph created with a centralized algorithm after a small number of protocol iterations. To fi nd suitable techniques for creating user communities, we analyze graphs created from real-world recommender datasets and identify specifi c properties of these datasets. Based on these properties, di fferent graph-based community-extraction techniques are chosen and evaluated. We select a technique that exploits identifi ed properties to create clusters of music listeners. The suitability of this technique is validated using a music dataset and two large movie datasets. On a graph of 6,040 peers, the selected technique assigns at least 85% of the peers to optimal communities, and obtains a mean classi cation error of less than 0.05% over the remaining peers that are not assigned to the best community.