Music Recommendation System Project Guide

With millions of songs available on platforms like Spotify, users often struggle to find new music that matches their taste. A smart music recommendation system enhances user satisfaction by suggesting songs tailored to individual preferences. Predicting what users might enjoy based on their listening history, genre preferences, and audio features like tempo, energy, and mood is a complex but rewarding challenge that involves deep personalization and data analysis.

Using Spotify’s extensive music datasets, machine learning models can analyze users' listening patterns and recommend tracks they are likely to enjoy. Collaborative filtering suggests songs based on similar users’ preferences, while content-based filtering uses track characteristics like tempo, danceability, and energy. Building a hybrid recommendation system combines both methods for better accuracy. This project blends creative AI applications, data science, and user experience design, making it a fascinating domain for innovation.

The system analyzes a user’s listening history and favorite tracks, then extracts audio features such as tempo, key, energy, and danceability. It uses collaborative filtering to find users with similar music preferences and content-based filtering to find songs with similar audio characteristics. Based on combined insights, the system generates highly personalized playlists that continuously evolve with the user’s changing tastes and habits.

• Collect Spotify track and user interaction data using Spotify Web API.
• Extract audio features like tempo, energy, loudness, danceability, and valence for each song.
• Implement collaborative filtering models like Matrix Factorization or KNN-based recommenders.
• Build content-based similarity models using cosine similarity between audio feature vectors.
• Deploy a hybrid system recommending personalized playlists and discover weekly songs for users.

• Spotify Web API Documentation
• LightFM Hybrid Recommender
• TensorFlow Recommenders Library