Spotify on Tuesday began rolling out a feature that allows listeners to exclude individual tracks from the “taste profile” the company uses to power personalized playlists and recommendations. The change, which appears in the track overflow menu on mobile and desktop, gives users a toggle to prevent a specific song from influencing their future Discover Weekly, Release Radar and algorithmic radio mixes.

Spotify said the feature, announced on its corporate blog and rolling out globally this week, is intended to address the familiar problem of one-off listening behavior warping recommendations. “Sometimes you play a soundtrack or a one-off novelty and you don’t want that to define your taste,” a Spotify spokesperson said. “This gives listeners direct control over how their listening signals are interpreted by our systems.”

Streaming services build user profiles from a mosaic of signals — plays, skips, saves, playlist adds and listening contexts — and even sparse or unusual signals can shift recommendation vectors, pushing users toward unwanted genres like white noise, sleep music or viral novelty tracks. For users who rely on music to, say, fall asleep, that can lead to unexpected downstream changes: more ambient or new-age suggestions when they were hoping for pop or jazz.

Recommender-system experts welcomed the move as a rare instance of giving consumers explicit agency over the inputs to opaque machine-learning models. “This is a pragmatic way to reduce noisy signals without asking users to retrain the entire system,” said Ethan Cole, an independent consultant who works with media recommendation algorithms. “It acknowledges that not all plays are equal.”

The rollout comes amid a broader shift at Spotify in response to industry concerns about AI and platform manipulation. In the same announcement, Spotify said it will begin labeling AI-generated music, expand spam-detection measures, and update its policy to limit exploitative practices that attempt to game discovery. Those additions reflect mounting regulatory and commercial pressure on platforms to distinguish synthetic content and protect both listeners and creators.

But the feature also raises questions about measurement and fairness. Artists and labels rely on algorithmic placements for discovery and revenue; enabling listeners to selectively exclude tracks from taste profiles could change the calculus of how songs accumulate influence in recommendation graphs. “Empowering users is the right move, but platforms must be transparent about the downstream effects on artist exposure,” said Miriam Alvarez, a media-economics researcher. She called for Spotify to publish impact studies showing how the change affects smaller artists and the long tail.

There is also the potential for gaming: organized groups could use exclusions strategically to manipulate neighborhood-based models, depriving or amplifying certain artists’ visibility. Spotify’s spokesperson said the company is monitoring for abuse and will rely on aggregate signals and other engagement metrics to limit manipulation.

For everyday listeners, the feature is likely to be most immediately useful as a guardrail against accidental taste drift. For the streaming economy and algorithm designers, it is a test case in balancing user control, platform integrity and the complex dynamics of automated curation. As platforms confront AI-era content and recommendation challenges, the modest toggle may be the start of a larger conversation about who gets to shape the music we hear.