Within Playlists
Who Really Curates a Streaming Playlist?
Modern playlists often blend editors, algorithms and behavioural data, making curation harder for listeners to see.
On this page
- How editors and algorithms share playlist decisions
- Why the same playlist brand can differ by listener
- How co listening data and audio features shape recommendations
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Introduction
A modern streaming playlist often looks deceptively simple: a title, a cover image and a sequence of songs. Behind that surface, however, sits a layered system of human editors, recommendation algorithms, behavioural data and catalogue analysis. This hybrid approach is often described as “algotorial” curation—a blend of algorithmic and editorial decision-making. Spotify itself uses the term to describe situations where editors and machine-learning systems work together rather than operating separately. [Spotify]newsroom.spotify.comThis collaboration is critical to the Spotify experience.Read moreSpotifyResponsibly Balancing What Goes Into Your Personalized…Mar 6, 2023 — We call this “algotorial”—bringing both the editorial and…
Understanding this hidden playlist machine helps explain why playlists became the new radio. Radio audiences once heard the same programme at the same time. Streaming platforms increasingly deliver playlist brands that appear identical while quietly changing from listener to listener. The result is a form of curation that feels personal but is often difficult to see, inspect or fully understand. [Spotify]support.spotify.comSpotifyTypes of Spotify playlistsFor some personalized playlists, our editors pick the pool of songs for the algorithms to select from fo… [Spotify]spotify.comSpotifyUnderstanding recommendations on SpotifyOther recommendations are tailored to each listener's unique taste, like a personalized pl…
How Editors and Algorithms Share Playlist Decisions
The common assumption is that playlists are either human-curated or algorithmically generated. In practice, many influential playlists occupy a middle ground.
Spotify states that some personalised playlists are built from pools of tracks selected by editors, after which algorithms decide which songs appear for individual listeners. In other cases, editors use audience and listening data to guide their selections while retaining final control over playlist composition. [Spotify]artists.spotify.combehind the playlists your questions answered by our playlist editorsSpotify for ArtistsBehind the Playlists: Your Questions Answered by Our…23 Jul 2020 — To give your track the best chance of getting pl…
This division of labour reflects the strengths of each side:
- Editors contribute cultural knowledge, genre expertise and awareness of emerging artists, scenes and releases.
- Algorithms contribute scale, analysing millions of listening sessions and adapting recommendations to individual users.
- Data systems measure engagement signals such as skips, saves, replays and listening duration, providing feedback about how tracks perform in different contexts. [Spotify Engineering]linkedin.comSpotify Engineering | Mike Warner | 17 commentsMay 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo…
Spotify Engineering describes algotorial playlists as products where human expertise and machine personalisation work together. Rather than replacing editors, recommendation systems help determine which versions of a playlist best fit different listeners. [Spotify Engineering]linkedin.comSpotify Engineering | Mike Warner | 17 commentsMay 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo…
This is a significant departure from broadcast radio. A radio station typically sends one sequence of songs to everyone. An algotorial playlist can operate more like a template whose final contents vary according to who is listening.
Why the Same Playlist Brand Can Differ by Listener
One of the least visible aspects of playlist culture is that a playlist name does not necessarily describe a fixed list of songs.
Spotify introduced personalised versions of certain editorial playlists so that listeners could receive different track selections under the same playlist brand. Reporting at the time noted that mood- and activity-focused playlists could contain algorithmically customised sections, meaning two listeners might encounter different versions of what appears to be the same playlist. [Pitchfork]pitchfork.comFirstly, certain editorial playlists will now include algorithmic, personalized content alongside human curation, meaning each listener w…
This approach solves a problem faced by streaming services. A playlist called “Workout”, “Dinner” or “Chill” attracts audiences with very different tastes. One listener may prefer electronic music, another indie rock, another contemporary pop. Instead of maintaining thousands of narrowly targeted playlists, platforms can personalise a single playlist identity.
The playlist brand therefore performs two jobs at once:
- It communicates a mood, activity or purpose.
- It acts as a delivery framework for personalised recommendations.
The listener sees a stable product. The underlying contents may be continuously adjusted according to listening history, region, age of account, followed artists, saved tracks and recent behaviour. [Spotify]artists.spotify.com– Spotify for ArtistsCreated by our editorial teams, powered by personalized data, or curated by you for your fans, each playlist is desi…
This flexibility helps explain why playlists increasingly function like radio formats rather than simple collections. The brand remains familiar even as the programme changes.
How Co-Listening Data and Audio Features Shape Recommendations
The recommendation systems behind playlist personalisation depend heavily on recognising patterns across large numbers of listeners.
One important technique is collaborative filtering, often described as co-listening analysis. Instead of focusing on the musical characteristics of a song, the system examines behavioural relationships. If many users who enjoy Artist A also regularly play Artist B, the platform can infer a connection even when the genres seem different on paper. Spotify’s recommendation architecture has long relied on this kind of behavioural matching. [WIRED]wired.comMusica Globalista: Spotify Discovery engineIt uses a hybrid approach by combining three types of recommendation models. Collaborative Filtering analyzes user behavior, Natural Lang…
A second layer analyses the music itself. Streaming platforms extract features from audio recordings and metadata, including characteristics associated with tempo, energy, mood, instrumentation and other musical traits. These signals help systems identify tracks that may fit together even when listener data is limited. [WIRED]wired.comMusica Globalista: Spotify Discovery engineIt uses a hybrid approach by combining three types of recommendation models. Collaborative Filtering analyzes user behavior, Natural Lang…
A simplified version of the process looks like this:
- The platform collects listening behaviour from millions of users.
- Algorithms identify clusters of similar listeners.
- Audio and metadata systems identify musical similarities between tracks.
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- Editorial teams define playlist concepts, genres, moods or candidate song pools. [orphiq.com]orphiq.comspotify editorial playlistUnderstanding Spotify Editorial Playlists15 Mar 2026 — How Spotify editorial playlists work, what editors look for, and how to increase y…
- Personalisation systems assemble final recommendations for individual users. [Spotify Engineering](#endnote-23 “
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May 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo") [Spotify The result is not purely machine-generated and not purely human-selected. It is an ongoi...
The Hidden Feedback Loop Behind Playlist Success
Algotorial systems create feedback loops that differ from traditional radio programming.
When a track appears in a playlist, the platform can immediately observe how listeners respond. Saves, replays, completion rates, skips and subsequent listening activity become signals that feed future recommendations. Songs that perform well may appear in more recommendation surfaces; songs that perform poorly may lose visibility. [Spotify for Artists](#endnote-6 “
Source snippet
Spotify for ArtistsBehind the Playlists: Your Questions Answered by Our...23 Jul 2020 — To give your track the best chance of getting pl")...
This creates a dynamic environment in which playlists are constantly learning from audience behaviour. Unlike radio, where audience measurement traditionally arrived through surveys, ratings panels or delayed analytics, streaming platforms can observe interactions almost instantly.
The consequence is that playlist curation becomes partly predictive and partly reactive. Editors may introduce a track because they believe it deserves attention. Algorithms then evaluate how audiences respond and adjust future exposure accordingly. [Spotify Engineering](#endnote-23 “
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May 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo")...
For listeners, this often feels seamless. For artists, it can mean that visibility depends not only on securing playlist placement but also on how audiences behave once the placement occurs.
Why the Playlist Machine Is Hard to See
Most listeners encounter only the front end of the system. They see playlist names, artwork and songs. They rarely see the multiple layers of selection operating underneath.
Part of this opacity comes from the blending of roles. A playlist can simultaneously reflect editorial taste, machine-learning predictions, catalogue strategy and audience behaviour. Because these layers are intertwined, it is often impossible for a listener to determine exactly why a particular song appeared.
Researchers studying platform culture increasingly describe this as a form of algotorial governance, where human and algorithmic decisions become difficult to separate. The recommendation system does not replace human judgement; instead, it reshapes how that judgement is exercised and scaled. [Sage Journals](#endnote-11 “
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Sage Journals“Your Wrapped doesn't lie”: Data realism and Spotify's...Feb 27, 2026 — In recommendation systems like Spotify, dataficatio")...
This hidden machinery is one reason playlists have become so powerful. They are not merely lists of songs. They are adaptive media products that combine editorial authority with continuous behavioural analysis. In doing so, they perform many of the functions once associated with radio—introducing music, organising attention and guiding discovery—while tailoring the experience to each listener in ways traditional broadcasting could never achieve. [Spotify Engineering](#endnote-23 “
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May 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo") <span class="citation-link-wrap"><a class="citation-inline-link" href="https://artists.spotify.co...
Endnotes
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Source: newsroom.spotify.com
Title: This collaboration is critical to the Spotify experience.Read more
Link: https://newsroom.spotify.com/2023-03-06/responsibly-balancing-what-goes-into-your-personalized-recommendations/Source snippet
SpotifyResponsibly Balancing What Goes Into Your Personalized...Mar 6, 2023 — We call this “algotorial”—bringing both the editorial and...
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Source: support.spotify.com
Link: https://support.spotify.com/us/artists/article/types-of-spotify-playlists/Source snippet
SpotifyTypes of Spotify playlistsFor some personalized playlists, our editors pick the pool of songs for the algorithms to select from fo...
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Source: spotify.com
Link: https://www.spotify.com/safetyandprivacy/understanding-recommendationsSource snippet
SpotifyUnderstanding recommendations on SpotifyOther recommendations are tailored to each listener's unique taste, like a personalized pl...
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Source: pitchfork.com
Link: https://pitchfork.com/news/spotify-to-personalize-specific-editorial-playlistsSource snippet
Firstly, certain editorial playlists will now include algorithmic, personalized content alongside human curation, meaning each listener w...
-
Source: wired.com
Title: Musica Globalista: Spotify Discovery engine
Link: https://www.wired.com/beyond-the-beyond/2017/10/musica-globalista-spotify-discovery-engineSource snippet
It uses a hybrid approach by combining three types of recommendation models. Collaborative Filtering analyzes user behavior, Natural Lang...
-
Source: artists.spotify.com
Title: behind the playlists your questions answered by our playlist editors
Link: https://artists.spotify.com/en/blog/behind-the-playlists-your-questions-answered-by-our-playlist-editorsSource snippet
Spotify for ArtistsBehind the Playlists: Your Questions Answered by Our...23 Jul 2020 — To give your track the best chance of getting pl...
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Source: artists.spotify.com
Link: https://artists.spotify.com/playlistingSource snippet
– Spotify for ArtistsCreated by our editorial teams, powered by personalized data, or curated by you for your fans, each playlist is desi...
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Source: artists.spotify.com
Link: https://artists.spotify.com/en/homeSource snippet
for Artists: Where Your Music is EverythingWith Spotify for Artists, you can amplify your reach, serve up videos, build pre-release hype...
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Source: newsroom.spotify.com
Title: prompted playlists algorithm gustav soderstrom
Link: https://newsroom.spotify.com/2025-12-10/spotify-prompted-playlists-algorithm-gustav-soderstrom/Source snippet
spotify.comYou're in Control: Spotify Lets You Steer the AlgorithmDec 10, 2025 — For the first time, your ideas, your logic, and your cre...
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Source: engineering.atspotify.com
Title: humans machines a look behind spotifys algotorial playlists
Link: https://engineering.atspotify.com/2023/04/humans-machines-a-look-behind-spotifys-algotorial-playlistsSource snippet
Spotify EngineeringHumans + Machines: A Look Behind the Playlists Powered by...27 Apr 2023 — Spotify has been working to create a better...
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Source: journals.sagepub.com
Link: https://journals.sagepub.com/doi/10.1177/14614448261422367Source snippet
Sage Journals“Your Wrapped doesn't lie”: Data realism and Spotify's...Feb 27, 2026 — In recommendation systems like Spotify, dataficatio...
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Source: theverge.com
Link: https://www.theverge.com/tech/694212/spotify-discover-weekly-playlist-listening-controls-personalization-genresSource snippet
Users will now see genre buttons like pop, R&B, and funk at the top of their playlists, allowing them to tailor [music recommendations]({{ 'algorithms-a7ef46/' | relative_url }}) to...
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Source: youtube.com
Link: https://www.youtube.com/watch?v=2i75sF-YTlcSource snippet
"Spotify Algotorial PlaylistsEditorial + Algprthmic playlists = Algotorial! Here is a link to the original blog post mentioned. [https://en..."](https://en...")...
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Source: andrmusic.co
Title: Spotify Metrics That Trigger Discover Weekly
Link: https://andrmusic.co/behind-the-music/spotify-metrics-trigger-discovery/Source snippet
Discover Weekly alone can generate 10,000-100,000+...Read more...
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Source: orphiq.com
Title: spotify editorial playlist
Link: https://orphiq.com/resources/spotify-editorial-playlistSource snippet
Understanding Spotify Editorial Playlists15 Mar 2026 — How Spotify editorial playlists work, what editors look for, and how to increase y...
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Source: orphiq.com
Title: spotify algorithmic playlists explained
Link: https://orphiq.com/resources/spotify-algorithmic-playlists-explainedSource snippet
15 Mar 2026 — Spotify algorithmic playlists are personalized playlists generated by Spotify's recommendation system based on listening be...
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Source: facebook.com
Link: https://www.facebook.com/groups/thedullclub/posts/2989816087890157/Source snippet
Spotify's Discover Weekly suggests only known songsEvery Monday I listen to Discover Weekly on Spotify which sometimes suggests some arti...
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Source: facebook.com
Link: https://www.facebook.com/maltadaily.mt/posts/spotify-has-introduced-a-new-feature-designed-to-give-listeners-greater-control-/1556561379625693/Source snippet
ms generate customized playlists and analyze each user's...Read more...
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Source: digitalrenaissance.education
Title: spotify for artists pitch guide how to get your music on editorial playlists
Link: https://www.digitalrenaissance.education/magazine/spotify-for-artists-pitch-guide-how-to-get-your-music-on-editorial-playlistsSource snippet
Spotify for Artists Pitch Guide for Editorial Playlists1 Mar 2026 — Learn how to pitch to Spotify editorial playlists using metadata, tim...
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Source: loopsolitaire.co.uk
Title: spotify algorithmic playlists
Link: https://loopsolitaire.co.uk/blog/spotify-algorithmic-playlists/Source snippet
Spotify Algorithm 2025: How to Get on Algorithmic PlaylistsLearn how the Spotify algorithm really works in 2025—and how you can get your...
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Source: loopsolitaire.co.uk
Link: https://loopsolitaire.co.uk/blog/spotify-editorial-playlists/Source snippet
ave on their featured artists, revealing and analyzing some in-depth data...
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Source: beatstorapon.com
Link: https://beatstorapon.com/blog/ultimate-guide-to-spotify-music-algorithm/Source snippet
Spotify's [Music Recommendation]({{ 'algorithms-a7ef46/' | relative_url }}) Algorithm: The Complete...1 Mar 2025 — Spotify's AI-Driven Playlist Curation (Discover Weekly, Release Ra...
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Source: linkedin.com
Title: Spotify Engineering | Mike Warner | 17 comments
Link: https://www.linkedin.com/posts/askmikewarner_humans-machines-a-look-behind-the-playlists-activity-7066249351949414400-U6iNSource snippet
May 21, 2023 — Editorial + Algorithmic = Algotorial Playlists. Here's a detailed explanation on how these playlists are created and custo...
Published: May 21, 2023
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Source: captechu.edu
Title: impact of automation and ai on the music industry
Link: https://www.captechu.edu/blog/impact-of-automation-and-ai-on-the-music-industrySource snippet
Spotify's Shift Away from Human-Curated PlaylistsMar 25, 2024 — Spotify's shift away from human-curated playlists towards automation refl...
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Source: musicbusinessworldwide.com
Link: https://www.musicbusinessworldwide.com/spotify-to-let-users-steer-the-algorithm-by-personalizing-playlists-with-ai-prompts/Source snippet
Spotify to let users 'steer the algorithm' by personalizing...11 Dec 2025 — Users can always fine-tune by editing the prompt, and can al...
Additional References
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Source: researchgate.net
Link: https://www.researchgate.net/publication/349324390_Playlists_and_the_Datafication_of_Music_FormattingSource snippet
(PDF) Playlists and the Datafication of Music FormattingThis chapter [charts]({{ 'charts/' | relative_url }}) the rise of the datafied playlist and argues that it is impor...
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Source: stereofox.com
Link: https://www.stereofox.com/articles/the-different-kinds-of-spotify-playlists-explained/Source snippet
The Different Kinds of Spotify Playlists ExplainedEditorial playlists formed by in-house curators are where you'd want to end up as an ar...
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Source: interspacemusic.com
Link: https://interspacemusic.com/blog/understanding-the-different-kinds-of-spotify-playlists-editorial-global-curation-personalized-and-active-sources/Source snippet
Understanding the Different Kinds of Spotify Playlists23 May 2025 — In this article, we'll break down the four major types of Spotify pla...
Published: May 2025
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Source: montrealethics.ai
Title: discover weekly how the music platform spotify collects and uses your data
Link: https://montrealethics.ai/discover-weekly-how-the-music-platform-spotify-collects-and-uses-your-data/Source snippet
How the Music Platform Spotify Collects and Uses Your Data26 May 2022 — Spotify collects all data that is entered by the artists: songs n...
Published: May 2022
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Source: music-tomorrow.com
Link: https://www.music-tomorrow.com/blog/how-spotify-recommendation-system-works-complete-guideSource snippet
Inside Spotify's Recommendation System: A Complete...1 Sept 2025 — Discover how Spotify's recommendation algorithms work...
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Source: d3.harvard.edu
Title: discover weekly how spotify is changing the way we consume music
Link: https://d3.harvard.edu/platform-rctom/submission/discover-weekly-how-spotify-is-changing-the-way-we-consume-music/Source snippet
Harvard Business School AI InstituteHow Spotify is Changing the Way We Consume Music13 Nov 2018 — Every week, Spotify generates a new pla...
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Link: https://hal.science/hal-04866759v1/file/henry-et-al-2024-impacts-of-ai-on-music-consumption-and-fairness.pdfSource snippet
Impacts of AI on Music Consumption and Fairnessby A Henry · 2024 · Cited by 19 — On services like Spotify, playlists serve as a standout...
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Source: pragmaticinstitute.com
Link: https://www.pragmaticinstitute.com/resources/articles/data/case-study-how-spotify-prioritizes-data-projects-for-a-personalized-music-experience/Source snippet
ls, the predictive recommendation engine generates playlists like “Discover Weekly...Read more...
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Source: illumin.usc.edu
Title: algorithmic symphonies how spotify strikes the right chord
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Symphonies: How Spotify Strikes the Right Chord21 Jan 2024 — This article explores Spotify's recommendation algorithm, including how it h...
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Source: maa1.medium.com
Title: human machine spotifys algotorial playlists 500c1a252723
Link: https://maa1.medium.com/human-machine-spotifys-algotorial-playlists-500c1a252723Source snippet
+ Machine: Spotify's 'Algotorial Playlists' | by MAA1As the name suggests, these playlists are a combination of editorial and algorithmic...
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