An Optimal Transformation for Discriminant and Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Using mixture models for collaborative filtering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Large-scale crossmedia retrieval for playlist generation and song discovery
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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We describe a way to measure the diversity of consumer's musical interests and characterize this diversity using published musical playlists. For each song in the playlist we calculate a set of features, which were optimized for genre recognition, and represent the song as a single point in a multidimensional genre-space. Given the points for a set of songs, we fit an ellipsoid to the data, and then describe the diversity of the playlist by calculating the volume of the enclosing ellipsoid. We compare 887 different playlists, representing nearly 29,000 distinct songs, to collections of different genres and to the size of our entire database. Playlists tend to be less diverse than a genre, and, by our measure, about 5 orders of magnitude smaller than the entire song set. These characteristics are important for recommendation systems, which want to present users with a set of recommendations tuned to each user's diversity.