How Music Apps Learn What You Don't Want in Playlists

This patent describes how a music streaming service learns what kinds of songs or artists a user dislikes for their playlists by tracking what they repeatedly ignore, then uses that information to avoid recommending similar things in the future.

What it covers

The system first shows a user a playlist and offers recommended songs to add to it (Claim 1). It then keeps track of how often songs with specific traits, called 'attributes' (like a certain artist, per Claim 3 and 4), are suggested for a playlist but the user doesn't pick them (Claim 1). If a user consistently ignores songs with a particular attribute, the system gives that attribute a lower score (Claim 1). This score is then used to reduce the chance of recommending other songs with that same attribute for playlist additions in the future (Claim 5). For example, if you keep skipping recommendations for songs by 'Artist X' when building a playlist, the system learns to show you fewer songs by 'Artist X' or similar artists for that playlist.

What it doesn't cover

• —Does not cover recommendations based solely on what a user likes or plays frequently, without considering ignored suggestions.
• —Does not cover general content recommendations outside the specific context of adding to an existing playlist.
• —Does not cover systems that track ignored individual songs without linking that behavior to broader 'attributes' like artist or genre.
• —Does not cover adjusting recommendations based on explicit 'dislike' buttons, only implicit ignoring by not selecting.
• —Does not cover recommendations where the user is not presented with a 'set of recommended media content items for addition to the playlist'.

The clever bit

The novelty lies in using repeated *non-selection* of recommended items for *playlist inclusion*, tied to specific *attributes*, to actively reduce the likelihood of future recommendations with those attributes. Most recommendation systems heavily weigh positive signals; this patent focuses on leveraging implicit negative feedback.

Why it matters

Personalization is critical for media streaming services. This patent describes a method for refining recommendations by learning from negative signals—specifically, what users repeatedly choose *not* to add to their playlists. This approach is crucial for improving user satisfaction and engagement by reducing irrelevant suggestions. Better recommendations can lead to users spending more time on a platform and discovering new content they genuinely enjoy, while avoiding content they dislike.

Real-world examples

• 1.Spotify's 'Enhance' feature for playlists
• 2.Playlist creation tools in music streaming applications
• 3.Personalized radio stations that adapt based on skipped tracks