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

TL;DR

SeER is a hybrid deep learning system that leverages MIDI content and collaborative filtering to improve song recommendations, address cold start issues, and generate personalized explanations, outperforming existing models.

Contribution

Introduces SeER, a novel hybrid model combining content-based sequence learning and collaborative filtering for explainable music recommendation.

Findings

SeER outperforms baseline systems in ranking accuracy.

Personalized explanations align with user preferences.

Effective in cold start scenarios.

Abstract

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