Playlists and personalised recommendations in ListenBrainz

Just in time for Christmas we are pleased to announce a new feature in our most recent release of ListenBrainz, the ability to create and share your own playlists! We created two playlists for each user who used ListenBrainz containing music that you listened to in 2020. Check out your lists at https://listenbrainz.org/my/recommendations. Read on for more details…

With our continuing work on using data in ListenBrainz to generate recommendations, we realised that we needed a place to store lists of music. That sounded like playlists to us, so we added them to ListenBrainz. As always, we did this work in the public ListenBrainz repository. You can now create your own playlists with the web interface or by using the API. Recordings in playlists map to MusicBrainz identifiers. If you’re trying to add something and can’t find it, make sure that it’s in MusicBrainz first.

Once you have a playlist, you can listen to it using our built-in BrainzPlayer, or export it to Spotify if you have an account there. If you have already linked your Spotify account to ListenBrainz you may have to re-authenticate and give us permission to create playlists on your behalf. Playlists can also be exported in the open JSPF format using the ListenBrainz API.

Over the last year we’ve started thinking about how to use data in MetaBrainz projects to generate recommendations of new music for people to listen to. For this reason, we started the Troi recommendation framework. This python package allows developers to build pipelines that take data from different sources and combine it in order to generate recommendations of music to listen to. We have already developed data sources using MusicBrainz, ListenBrainz, and AcousticBrainz. If you are a developer interested in working on recommendations in the context of ListenBrainz we encourage you to check it out.

Now that we can store playlists we needed some content to fill them with. Luckily we have some great projects worked on by students over the last few years as part of MetaBrainz’ participation in the Google Summer of Code project, including this year’s work on statistics and summary information by Ishaan. Using Troi and ListenBrainz statistics, we got to work. Every user who has been contributing data to ListenBrainz recently now has two brand new 2020 playlists based on the top recordings that you listened to in 2020 and the recordings that you first listened to in 2020. If you’re interested in the code behind these playlists, you can see the code for each (top tracks, first tracks) in the troi repository.

If you’re a long-time user of ListenBrainz you may be familiar with the problem of matching your listens to content in MusicBrainz to be able to do things with it. We’ve been working hard on a solution to this problem and have built a new tool using typesense to provide a quick and easy way to search for items in the MusicBrainz database. You are using this tool when you create a playlists using the web interface and search for a recording to add. This is still a tech preview, but in our experience it works really well. Thanks to the team at typesense for helping us with our questions over the last few weeks!

This work is still in its early days. We thought that this was such a great feature that we wanted to get it out in front of you now. We’re happy to take your feedback, or hear if you are having any problems. Open a ticket on our bug tracker, come and talk to us on IRC, or @ us. Did we give you a bad jam? Sorry about that! We’d love to have a conversation about what went well and what didn’t in order to improve our systems. In 2021 we will start generating weekly and daily playlists for users based on your recent listens using our collaborative filtering recommendations system.

Merry Christmas from the whole MetaBrainz team!