Music Cold-start and Long-tail Recommendation: Bias in Deep Representations

Recent advances in deep learning have yielded new approaches for music recommendation in the long tail. The new approaches are based on data related to the music content (i.e. the audio signal) and context (i.e. other textual information), from which it automatically obtains a representation in a latent space that is used to generate the recommendations. The authors of these new approaches have shown improved accuracies, thus becoming the new state-of-the-art for music recommendation in the long tail. One of the drawbacks of these methods is that it is not possible to understand how the recommendations are generated and what the different dimensions of the underlying models represent. The goal of this thesis is to evaluate these models to understand how good are the results from the user perspective and how successful the models are to recommend new artists or less-popular music genres and styles (i.e. the long tail). For example, if a model predicts the latent representation from the audio but a given genre is not well represented in the collection, it is not probable that the songs of this genre are going to be recommended. First, we will focus on defining a measure that could be used to assess how successful a model is recommending new artists or less-popular genres. Then, the state-of-the-art methods will be evaluated offline to understand how they perform under different circumstances and new methods will be proposed. Later, using an online evaluation it will be possible to understand how these recommendations are perceived by the users. Increasingly, algorithms are responsible for the music that we consume, understanding their behavior is fundamental to make sure they give the opportunity to new artists and music styles. This work will contribute in this direction, making it possible to give better recommendations for the users.