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About this class diagram for Spotify
This class diagram gives an overview of Spotify by depicting a simplified version of such a music streaming system. It shows how different users and classes in the system collaborate and interact to achieve their specific tasks. Users, playlists, tracks, albums, artists, subscriptions, and recommendations are useful classes that interact with one another.
The "user" is the center of attraction in this diagram due to its connection with other classes. This represents the ability of the user to perform many tasks inside the system. It contains the attributes such as the name, ID, email, and subscription type he wants to get.
The "user" entity is directly responsible for making and managing playlists, following artists, playing tracks, and rating them. Users also have subscriptions that may be renewed or terminated, demonstrating the system's flexibility to manage different subscription kinds and dates. The users can also receive the recommendation according to their music taste.
The "playlist" class allows users to create customized collections of music according to their preferences. The songs can either be added to or deleted from these playlists. Playlists can have many songs and highlight how consumers may customize their listening experience. The playlists have unique IDs, creation dates, titles, and descriptions.
Tracks are part of albums generated by artists, and they show how music content is arranged inside the system. The track can be played at any time and contains specific details. These can be created using a title, duration, album, artist, and an ID.
The "artist" is an external class that creates tracks and albums. These are placed in the system from where a user can access these albums or tracks. Artists' attributes include a name, genre, and bio, which assist in categorizing and providing updates and information about the released albums available on the system.
The "recommendation" class describes the ability of the system to make personalized music recommendations based on user activity. Recommendations are connected to specific users and tracks, allowing the system to improve the user experience by providing music that matches their preferences.