Hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behaviour of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms.

Related publications

P. Chiliguano and G. Fazekas, “Hybrid music recommender using content-based and social information”, 2016.
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