Recommendation System based on Artist and Music Embeddings

In this paper, we present a personalized music recommendation system based on the embeddings of artists and music. The main peculiarity of our work is that the determining factors of a user’s preferences for a developed music recommender system are the artists and the music that they listen to. The artist embeddings inform the network about the contextual representation of artists in a latent space, where similar artists are closer to each other. The music embeddings hold the information about the music. Both embeddings are then combined to form a new embedding, which is then used to predict the user’s preferences. We use the Spotify’s API to collect the data to train and evaluate the model. Two approaches of building a music recommender system are considered in this paper. Each approach significantly differs in the way the embeddings are learned.