  State-of-the-art music recommender systems are based on collaborative
filtering, which builds upon learning similarities between users and songs from
the available listening data. These approaches inherently face the cold-start
problem, as they cannot recommend novel songs with no listening history.
Content-aware recommendation addresses this issue by incorporating content
information about the songs on top of collaborative filtering. However, methods
falling in this category rely on a shallow user/item interaction that
originates from a matrix factorization framework. In this work, we introduce
neural content-aware collaborative filtering, a unified framework which
alleviates these limits, and extends the recently introduced neural
collaborative filtering to its content-aware counterpart. We propose a
generative model which leverages deep learning for both extracting content
information from low-level acoustic features and for modeling the interaction
between users and songs embeddings. The deep content feature extractor can
either directly predict the item embedding, or serve as a regularization prior,
yielding two variants (strict and relaxed) of our model. Experimental results
show that the proposed method reaches state-of-the-art results for a cold-start
music recommendation task. We notably observe that exploiting deep neural
networks for learning refined user/item interactions outperforms approaches
using a more simple interaction model in a content-aware framework.
