#Spotify jobs playlisting movie#

In broad strokes, we use various machine learning techniques to analyze a user’s listening history to better predict which songs they will want to listen to. The end result of these combined efforts? A new Algotorial-powered, personalized editorial playlist designed for road trips: Songs to Sing in the Car. We will display the image of the artist for which the user has the most affinity. For example, a ’60s Rock playlist might have one image with a British Invasion artist and another with a Surf Rock artist. They can further personalize by having the imagery optionally chosen based on the listener’s tastes.

With personalization, this one playlist can suit a wide variety of listeners while trusting that all of the candidates are still “singable”.įinally, the editor works with the Spotify design team to brand the playlist’s title, description, and imagery. In our road trip example, the playlist might have a mix of Pop, Indie, Rock, and Hip-Hop in the pool. This is particularly helpful for playlists on broad topics. They can pick songs that will appeal to a wide range of listeners.Īfter the pool is created, the algorithms take over, picking out the appropriate tracks and placing them in order for each given user. They don’t need to aim for a balance that will make everyone happy. They also have access to metrics to see what tracks have been performing well in the playlist and which have not.īecause the editors are choosing potential candidates instead of the exact order of the final playlist, they can expand the pools to a wider range of tastes and not just the most obvious and popular tracks. This large pool combines their musical and cultural expertise, as well as advanced filtering for searches they’ve conducted to help narrow down the vast universe of possible songs to the most relevant ones.

The editor collects tracks that could be appropriate for the playlist and adds them to what we call a “pool”. It’s hard to describe why, but you know it when you hear it.

#Spotify jobs playlisting movie#

Maybe it appeared in a TV show or movie recently to remind you of your teenage years. It might be the song that was on repeat last summer, or a particularly catchy refrain that is repeated over and over. Knowing what songs might be particularly “singable” is difficult to describe in algorithms. Once the user’s need is defined, the editor creates a content hypothesis - in our road trip example, the editor decides the content hypothesis could be “familiar songs you know all the words to, and would sing along to”. Our editors begin by envisioning a specific user need - let’s take an activity like a road trip, for example. The process of creating Personalized Editorial Playlists starts with the editors. Creating a playlist with Algotorial technology So by combining a user’s listening history with the relationships across tracks, we can create a unique list of songs for each one of our users, based on what we think they will like. These algorithms take a look at the audio attributes of music and can find similarities across tracks to identify what songs are often listened to together. While the RapCaviar playlist is owned by our Editorial team, others like Discover Weekly, Daily Mix, and Your Time Capsule are powered by our Personalization Algorithms.

This is particularly powerful as it allows editors to be aware of small trends or cultural events that drive the ever-changing way users are consuming music. This specialization allows them to have a deeper understanding of the music that artists have created and the ways users listen to them. One editor might concentrate on Hip-Hop while another editor focuses on the music being produced and listened to in Brazil.

Some of these playlists, such as RapCaviar, created and maintained by our Editorial team, are distinctive based on genres or localities. Spotify has a library of playlists for almost every occasion, be it different moods, activities, genres, eras, new music, top hits, emerging artists, cultural moments, or regional listening trends. The outcome of these efforts has resulted in the technology we call “Algotorial.” The best of both worlds: Editorial and Algorithms TL DR Since 2017, Spotify has been working to create a better listening experience for our users by creating algorithmically personalized playlists powered by the expertise of our curators.