Music recommendation based on artist novelty and similarity

Abstract

Most existing systems recommend songs to the user based on the popularity of songs and singers. However, the system proposed in this paper is driven by an emerging and somewhat different need in the music industry-promoting new talents. The system recommends songs based on the novelty of singers (or artists) and their similarity to the user's favorite artists. Novel artists whose popularity is on the rise have a higher priority to be recommended. Specifically, given a user's favorite artists, the system first determines the candidate artists based on their similarity with the favorite artists and then selects those who have a higher novelty score than the favorite artists. Then, the system outputs a playlist composed of the most popular songs of the selected artists. The proposed system can be integrated into most existing systems. Its performance is evaluated using the Spotify Radio Recommender as a reference and a pool of 100 subjects recruited on campus. Experimental results show that our system achieves a high novelty score and a competitive user-preference score.