An Effective Graph-based Music Recommendation Algorithm for Automatic Playlist Continuation

Automatic playlist continuation (APC) is now essential in music streaming platforms that enable users to discover new music tracks and artists with a seamless interface. To achieve attractive user experiences, it is vital to recommend music tracks that meet the users' interests. However, it is difficult for existing recommendation methods to find effective tracks since the platform includes massive music tracks associated with complex property relationships. In this paper, we propose a novel recommendation algorithm for effective APC. To improve the recommendation accuracy, our algorithm excludes unpromising properties by using a biased graph-based search method. Our extensive experiments on real-world playlists clarify that our algorithm outperforms the state-of-the-art methods in terms of recommendation accuracy.