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This repository was archived by the owner on Mar 27, 2025. It is now read-only.
This repository was archived by the owner on Mar 27, 2025. It is now read-only.

Create accounts for your music taste and share your favourite albums #3

Description

@ArdaHalim2

Issue Description:

We need to create entities to represent users, albums, their favorite albums, and genres. Additionally, we need to implement AI-driven album recommendations based on user preferences, including favorite albums and genres.

Requirements:

Entities:

  • User: Represents users in the system with attributes like username, email, and password.
  • Album: Represents music albums with details such as title, artist, and release year.
  • FavoriteAlbum: A join table that links users to their favorite albums (Many-to-Many relationship between User and Album).
  • Genre: Represents different music genres that users can associate with their favorite albums and music taste.
  • AI Album Recommendations:

Recommendation Logic: Implement AI-driven recommendations based on:

  • A user's favorite genres and albums.

  • A user's music taste (e.g., through analysis of previously liked genres and albums).

  • Algorithm can be based on collaborative filtering or content-based filtering.

  • Relationships Between Entities:

  • User ↔ FavoriteAlbum ↔ Album: Users can have multiple favorite albums, and each album can be favorited by many users.

  • User ↔ Genre: A user can have multiple favorite genres (Many-to-Many relationship).

  • AlbumRecommendation: This entity will store AI-driven album recommendations based on user preferences. Each recommendation will be linked to a user and an album, with a reason for the recommendation.

User Interface:

  • Display album recommendations to users on their dashboard.
  • Allow users to interact with recommendations (e.g., mark as liked, skip).

AI Integration:

  • Integrate a machine learning model or recommendation algorithm to generate personalized album suggestions for users.
  • Provide a backend service that regularly updates recommendations based on user activity.

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