Abstract
In this chapter, we present a content-based RS for music files, called MUSIPER, in which individualized (subjective) music similarity perception models of the system users are constructed from objective audio signal features by associating different music similarity measures to different users. Specifically, our approach in developing MUSIPER is based on investigating certain subsets in the objective feature set and their relation to the subjective music similarity perception of individuals.
The acronym MUSIPER stands for MUsic SImilarity PERception.
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Lampropoulos, A.S., Tsihrintzis, G.A. (2015). Similarity Measures for Recommendations Based on Objective Feature Subset Selection. In: Machine Learning Paradigms. Intelligent Systems Reference Library, vol 92. Springer, Cham. https://doi.org/10.1007/978-3-319-19135-5_5
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DOI: https://doi.org/10.1007/978-3-319-19135-5_5
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