music-recommender

Generate mood-based playlists by fetching track metadata from the Spotify API and filtering songs by BPM, energy, and emotion tags to match your specified scene or mood. This produces a personalized playlist tailored to your listening scenario.

Yes, you can create a focused work playlist by applying filters for low energy and steady BPM to the recommended tracks. This ensures a consistent listening experience suitable for concentration and deep work sessions.

Yes, integrating with external music metadata sources like Spotify and Last.fm requires valid API credentials to fetch track data, artist information, and recommendations needed to generate personalized playlists.

Filtering by BPM, energy, and emotion tags ranks tracks to ensure a consistent listening experience matching your desired mood or scene. This style and tempo matching prevents jarring transitions in your generated playlist.

The best way to curate music for scenarios like commuting or sleep is using scene-aware recommendation commands that match tracks to specific emotional states and activities, saving time compared to manual searching.

Yes, the music recommender persists basic usage records in a local data directory when generating recommendations and playlists. This auditing data helps refine future music recommendations based on your past listening preferences.