Music Recommendation System Based on Artist Relatedness and Audio Similarity

The automatic music recommendation system has become an increasingly relevant problem in recent years, along with the increasing amount of music circulating in digital format.In this research, music recommendations are sought by searching for music that is similar to music input, using value of music features with K-Nearest Neighbor method. Artist relatedness also be used in this research to get music recommendations, so that the recommendations are suitable with the listener’s preferences. Spotify API which is provided by Spotify, an online music platform is used in searching music features and artist relatedness in this research. The method used to calculate audio similarity is K-Nearest Neighbor (K-NN). Based on evaluation result, music recommendations that only use artist relatedness features have a higher precision value compared to music recommendations that use combination of artist relatedness and audio similarity, because the research participants were more likely (subjectively) to choose popular music compared to music that has similar audio with input music.