cwb-profile

To generate a personalized music profile, you need to analyze your listening history to produce detailed taste reports. This process extracts your top artists, genres, and language breakdowns to create clear summaries of your changing preferences over time.

Yes, you can get history-aware music recommendations tailored to specific moods or scenes like coding, commuting, or relaxing. The analysis uses your prior play patterns to produce personalized search ideas that match your requested activity context.

The best way to analyze daily or weekly listening summaries is to use period detection on your raw listening history. This surfaces your top played artists and genre breakdowns, turning historical play data into actionable insights for the selected timeframe.

Music taste analysis does not work without enough historical play data. An error-aware workflow detects insufficient listening history and explicitly tells you when there is not enough data to build a meaningful profile or generate a report.

You can track how your music taste changes over a month or year by generating period-based listening reports. These reports analyze your historical play patterns to surface evolving preferences, top genres, and language breakdowns for the specified timeframe.