Fuzzy techniques in the usage and construction of comparison measures for music objects

Klaas Bosteels
Ghent University, Belgium (October, 2009)


Many of the algorithms and heuristics designed to support users in accessing and discovering music require a comparison measure for artists, songs, albums, or other music objects. In this thesis, we discuss how fuzzy set theory can be useful in both the usage and construction of such measures.

After showing that music objects can naturally be represented as fuzzy sets, we introduce a triparametric family of fuzzy comparison measures that can be used to systematically generate measures for comparing such representations. The main advantage of this family is that it paves the way for a convenient threestep approach to constructing a computationally efficient comparison measure for music objects that meets all requirements of a given application. We illustrate this by means of two practical examples, the second one being the construction of the underlying comparison measure for the popular “Multi Tag Search” demonstration on Last.fm’s Playground, which recently graduated to the main Last.fm website in the form of “Multi Tag Radio”.

For the usage of comparison measures for music objects, we focus on one specific application, namely, dynamic playlist generation. More precisely, we discuss heuristics that leverage the skipping behaviour of the user to intelligently determine the song to be played next. Using fuzzy set theory, we introduce a formal framework that makes the definitions of such heuristics systematic, concise, and easy to analyse and interpret. As an illustration of the latter benefit, we rely on this framework to relate the performance of some of the considered dynamic playlist generation heuristics to certain user behaviour. We also clearly confirm these theoretical insights by means of a new methodology for evaluating playlist generation heuristics based on listening patterns extracted from radio logs.

To conclude, we present the software that was employed and developed in order to be able to implement some of the described data-intensive experiments and applications.

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