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State of the art music recommender systems mainly rely on either matrixfactorization-based collaborative filtering approaches or deep learningarchitectures. Deep learning models usually use metadata for content-basedfiltering or predict the next user interaction by learning from temporalsequences of user actions. Despite advances in deep learning for songrecommendation, none has taken advantage of the sequential nature of songs bylearning sequence models that are based on content. Aside from the importanceof prediction accuracy, other significant aspects are important, such asexplainability and solving the cold start problem. In this work, we propose ahybrid deep learning model, called "SeER", that uses collaborative filtering(CF) and deep learning sequence models on the MIDI content of songs forrecommendation in order to provide more accurate personalized recommendations;solve the item cold start problem; and generate a relevant explanation for asong recommendation. Our evaluation experiments show promising results comparedto state of the art baseline and hybrid song recommender systems in terms ofranking evaluation. Moreover, based on proposed tests for offline validation,we show that our personalized explanations capture properties that are inaccordance with the user's preferences.

SeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System