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

Neural content-aware collaborative filtering for cold-start music recommendation