  Recently deep learning based recommendation systems have been actively
explored to solve the cold-start problem using a hybrid approach. However, the
majority of previous studies proposed a hybrid model where collaborative
filtering and content-based filtering modules are independently trained. The
end-to-end approach that takes different modality data as input and jointly
trains the model can provide better optimization but it has not been fully
explored yet. In this work, we propose deep content-user embedding model, a
simple and intuitive architecture that combines the user-item interaction and
music audio content. We evaluate the model on music recommendation and music
auto-tagging tasks. The results show that the proposed model significantly
outperforms the previous work. We also discuss various directions to improve
the proposed model further.
