Authors: Daniel Wolff and Tillman Weyde
Predicting user’s tastes on music has become crucial for a competitive music recommendation systems, and perceived similarity plays an influential role in this. MIR currently turns towards making recommendation systems adaptive to user preferences and context. Here, we consider the particular task of adapting music similarity measures to user voting data. This work builds on and responds to previous publications based on the MagnaTagATune dataset. We have reproduced the similarity dataset presented by Stober and Nürnberger at AMR 2011 to enable a comparison of approaches. On this dataset, we compare their two-level approach, defning similarity measures on individual facets and combining them in a linear model, to the Metric Learning to Rank (MLR) algorithm. MLR adapts a similarity measure that operates directly on low-level features to the user data. We compare the different algorithms, features and parameter spaces with regards to minimising constraint violations. Furthermore, the effectiveness of the MLR algorithm in generalising to unknown data is evaluated on this
dataset. We also explore the effects of feature choice. Here, we find that the binary genre data shows little correlation with the similarity data, but combined with audio features it clearly improves generalisation.