Deep content-based music recommendation
Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.
This is an interesting paper that addresses a problem 
      of significant practical value. Specifically, the authors propose a 
      technique to learn latent factors directly from music audio without 
      relying on usage data. They do this using deep convolutional neural 
      networks. The paper is well written and the authors do a good job of 
      placing this work in context. The experiments are reasonable. They provide 
      quantitative metrics as well as qualitative results, where appropriate. I 
      particularly like Table 1 and Figure 4. Using existing machine 
      learning algorithms, the authors provide a nice solution to a hard problem 
      in which there is little previous work. The solution would be very useful 
      in practice. However, there is no algorithmic machine learning 
      contribution. Nevertheless, it could be a good applications paper at NIPS, 
      but it is very clearly an applications paper.