Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Automatic Video Summarization by Affinity Propagation Clustering and Semantic Content Mining
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Finding image exemplars using fast sparse affinity propagation
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Getting recommender systems to think outside the box
Proceedings of the third ACM conference on Recommender systems
Song Clustering Using Peer-to-Peer Co-occurrences
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Constructing treatment portfolios using affinity propagation
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Escape the bubble: guided exploration of music preferences for serendipity and novelty
Proceedings of the 7th ACM conference on Recommender systems
Classification accuracy is not enough
Journal of Intelligent Information Systems
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This work proposes a method based on the affinity propagation clustering technique to classify artists and find representative artists for each musical category ("musical world") using only the listening history log of a music service. Two variants of the proposed method are compared with a classic k-means clustering approach and an evaluation based on folksonomy analysis is provided. The results suggest that affinity propagation is highly effective in the music domain, allowing for better classification of artists than classic clustering techniques. Furthermore, an analysis of the results indicates that classifying music by genres, even using more than one genre for each artist, is sometimes an oversimplification of the dynamics that govern the music ecosystem. While most of the clusters found have a strict relationship with a music genre, the characterization of some of the emerged "musical worlds" is related to other aspects like the geographic origin of the artists, the prominent themes in the lyrics, the evocative potential and the association with a culture/lifestyle or the context in which the music has been used.