Publication: Research - peer-review › Article in proceedings – Annual report year: 2007
Nowadays there is an increasing interest in developing methods for building music recommendation systems. In order to get a satisfactory performance from such a system, one needs to incorporate as much information about songs similarity as possible; however, how to do so is not obvious. In this paper, we build on the ideas of the Probabilistic Latent Semantic Analysis (PLSA) that has been successfully used in the document retrieval community. Under this probabilistic framework, any song will be projected into a relatively low dimensional space of "latent semantics", in such a way that that all observed similarities can be satisfactorily explained using the latent semantics. Additionally, this approach significantly simplifies the song retrieval phase, leading to a more practical system implementation. The suitability of the PLSA model for representing music structure is studied in a simplified scenario consisting of 10.000 songs and two similarity measures among them. The results suggest that the PLSA model is a useful framework to combine different sources of information, and provides a reasonable space for song representation.
|Title||2007 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING. Formerly the IEEE Workshop on Neural Networks for Signal Processing, August 27-29, 2007, Thessaloniki, Greece|
|Workshop||2007 IEEE International Workshop on Machine Learning for Signal Processing|
|Period||27/08/07 → 29/08/07|
|Citations||Web of Science® Times Cited: No match on DOI|
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