Music Recommendation System

[sharayuanuse]

Is your feature request related to a problem? Please describe.

Many music streaming platforms recommend songs based on trending content or basic genre preferences. However, these recommendations often lack personalization tailored to an individual's unique listening habits, such as mood, activity (e.g., workout, study), and time of day. This results in user frustration, as they frequently need to manually search for songs or playlists that match their immediate preferences, leading to an inefficient and less enjoyable music discovery experience.

Describe the solution you'd like

I would like a personalized music recommendation system that learns from users' listening history, mood preferences, and patterns (e.g., time of day, activity). The system should dynamically suggest songs based on factors like recently played tracks, favorite genres, and user-defined moods (e.g., energetic, calm). Additionally, it could offer curated playlists for specific activities (e.g., workout, relaxation), and recommend new music similar to artists or genres the user listens to frequently.

Describe alternatives you've considered

• Collaborative Filtering: Recommend songs based on what similar users listen to, but this could lead to less personalized suggestions and may not adapt to changes in individual preferences quickly.
• Mood-based Tagging: Implement a system where users manually tag songs with moods or activities, but this would rely heavily on user input and might be time-consuming for them.
• Genre-based Recommendations: Suggest songs only based on genre preferences, though this may not fully capture more nuanced aspects like user mood, time of day, or activity.

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[github-actions[bot]]

Thanks for raising this issue! However, we believe a similar issue already exists. Kindly go through all the open issues and ask to be assigned to that issue.

[github-actions[bot]]

Hello @sharayuanuse! Your issue #503 has been closed. Thank you for your contribution!