Juggle: large-scale discovery in music recommendation

Today’s offer of audio content exceeds the human capability of manually searching datasets with hundreds of songs, demanding automated tools capable of handling music recommendation when faced with large-scale collections. In this work, we address the playlist generation and song discovery tasks with large-scale datasets. It is possible to quickly obtain playlists and explore collections with examplebased queries using audio features, lyrics and tags. We developed a music discovery prototype to demonstrate this content based approach. This demo is based on the Million Song Dataset, a large-scale collection of audio features and associated text data comprising almost 300 GB of information.