What's cooking in the Last.fm playlisting lab

Thursday, 27 September 2012
by mark
filed under About Us
Comments: 47

In the Music Information Retrieval team here at Last.fm we’re currently developing a new generation of smart playlisting engines, and we’d like take the chance to give you a sneak preview of what they can do, as well as explaining a bit more about playlisting services in general.

You can think of playlisting engines as falling into two categories: one repeatedly chooses which track to stream next when you’re listening to an internet radio station like any of Last.fm’s radio stations; the other selects a single set of tracks from a collection all in one go, like iTunes genius or Google Music’s instant mix. While in theory these do similar jobs, as every good scientist knows, the difference between theory and practice is greater in practice than it is in theory, and in practice the requirements for these two types of playlists can be very different. Our new generation service is designed to provide instant playlists from collections of any size, and you can try a demo right now, or read on to find out more.

Last.fm instant playlisting

We’ll talk a bit more about radio playlisting in a separate post, but one of the main characteristics required from the other type of engine is the ability to choose from music collections of wildly varying sizes. Our existing engines have mostly been targeted at very large commercial catalogues containing millions of recordings – you can see them at work in the Last.fm Spotify app (start playing any track, go to the Now Playing tab in the app and click “Similar Tracks Playlist”).

The new generation of engines is designed to continue to do a really good job when choosing tracks from small personal collections. In practice that means we can’t rely on any single type of information to tell us which other tracks might be a good match for any particular playlist. Luckily thanks to your scrobbles and tags, and a bit of audio analysis and machine learning magic on our side, we have three independent types of information linking artists and tracks. Another new feature is the ability to generate playlists based on mood and other musical properties. Finally when playlisting from personal collections we’ve been able to experiment with ways of choosing the sequence of tracks that aren’t restricted by licensing rules.

But we know we still have a huge amount to learn before any machine can approach the skill of a human DJ, so we’ve built a simple demo to let you try out the services. Please let us know how you think we’re doing and we’ll incorporate your feedback into our final version of the new engines. Thanks for listening!