An algorithmic playlist is assembled by software rather than by a person. The service looks at what each listener plays, skips and saves, compares it with the habits of people with similar taste, and builds a personal selection that changes over time.
Spotify’s Discover Weekly, introduced in July 2015, made the idea mainstream: about two hours of music, refreshed every Monday for each user and adapting as tastes change. Release Radar, built around new music from followed artists, is another example; on Spotify, a song pitched to editors at least a week before release also reaches followers’ Release Radar.
For listeners these lists are an easy discovery tool. For artists, they are widely thought to respond to genuine engagement, such as saves and repeat plays, but the exact workings are not public, so promises of guaranteed tricks to beat the algorithm deserve scepticism. In lo-fi, where many tracks share a similar mood, algorithms often decide which producer a study session lands on next.