Personalized Music Recommendation System

Description:

The Personalized Music Recommendation System project aims to develop a web application that offers customized music recommendations to users based on their music preferences, listening history, and mood. Leveraging machine learning algorithms and collaborative filtering techniques, the application will provide tailored music suggestions to enhance users' music discovery experience and enjoyment.

Features:

User Profiling: Analyzes users' music preferences, listening habits, and mood to create personalized profiles.

Recommendation Engine: Utilizes machine learning algorithms such as collaborative filtering, content-based filtering, and matrix factorization to generate accurate and relevant music recommendations.

Customization Options: Allows users to specify music genres, artists, moods, and activity types to receive personalized music suggestions tailored to their preferences.

Playlist Generation: Creates customized playlists based on user preferences, mood, or activity type (e.g., workout, relaxation).

Real-Time Updates: Provides real-time recommendations based on user interactions and dynamically adjusts suggestions as users' preferences evolve.

Social Integration: Enables users to connect with friends, share playlists, and discover new music together, fostering a sense of community and social engagement.

Cross-Platform Compatibility: Supports integration with various music streaming platforms and devices, ensuring accessibility across desktop and mobile devices.