A0538
Title: Biclustering listeners and music genres using a composite likelihood-based approach
Authors: Monia Ranalli - Sapienza University of Rome (Italy)
Francesca Martella - La Sapienza University of Rome (Italy) [presenting]
Abstract: A finite mixture model is proposed that simultaneously clusters listeners' habits and music genres. By following the underlying response variable (URV) approach, the music genres are treated as discretized versions of latent continuous variables, which are distributed according to a mixture of Gaussians. To introduce a partition of the music genres within each mixture component, a factorial representation of the data is used, where a binary row stochastic matrix represents music genre membership. This method allows associating each mixture component with a cluster of music genres, thereby defining a bicluster of listeners' habits and music genres. Given the numerical complexity of the likelihood function, model parameters are estimated using a composite likelihood (CL) approach, leading to a computationally efficient, like EM algorithm. The results illustrate the effectiveness of the proposed model in discovering significant patterns within the data. The model adeptly identifies clusters of listeners who share similar preferences for clusters of music genres, revealing both the listener groups with common tastes and the relationships between different music genres within these groups. Additionally, by allowing the number of clusters of music genres to vary with listener clusters, the model adeptly captures the inherent variability in listener preferences, exhibiting its flexibility and accuracy in representing the data and uncovering interesting patterns in listener behavior.
