The Machine Learning Math Behind My Music Recommendation App

Unveiling the Math Behind Aria's Music Recommendation System (v1)

Level up your music discovery! In this video, we decode the k-means clustering-inspired algorithm that powered Aria's v1 music recommendation system.

Here's what you'll learn:

Harnessing Spotify's API: Discover how to securely acquire user data to fuel your recommendations.
Quantifying Musical Preferences: Learn how to extract audio features that unveil a user's musical taste.
K-Means Clustering for Song Grouping: Grasp the power of k-means clustering for grouping similar songs.
Distance-Based Recommendations: Dive deep into the logic behind recommending songs based on user data and song similarity.
[Full Project Review Linked in Cards & Description]

Important Note: Due to recent Spotify API updates, replicating this exact approach with their data isn't possible. However, this video offers valuable insights into music recommendation system design.

Let's get the conversation started! Share your thoughts, alternative ideas, or questions in the comments below.

Most Recent Dev Log: https://rumble.com/v4hlj7o-aria-dev-log-2-integrating-the-spotify-player-music-platform-with-flutter-f.html

Project Review: https://rumble.com/v4hlgjl-project-review-a-music-recommendation-machine-learning-app-with-flutter-and.html