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MatchMus

Completed 2024 • Microservices Architecture

MatchMus is a comprehensive music streaming platform that combines traditional music library management with AI-powered song recommendations. Built with a microservices architecture, it features a React frontend, NestJS backend, and a Flask-based recommendation service. The platform enables users to discover music through content-based filtering algorithms, create and manage playlists, interact with content through comments and likes, and communicate via real-time chat. An admin dashboard provides analytics and content management capabilities.

Machine Learning Full-Stack Development Data Science Software Engineering DevOps Real-time Applications

Overview

MatchMus is a comprehensive music streaming platform that combines traditional music library management with AI-powered song recommendations. Built with a microservices architecture, it features a React frontend, NestJS backend, and a Flask-based recommendation service. The platform enables users to discover music through content-based filtering algorithms, create and manage playlists, interact with content through comments and likes, and communicate via real-time chat. An admin dashboard provides analytics and content management capabilities.

Key Features

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AI-powered song recommendations using content-based filtering

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Music streaming with audio player

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Playlist creation and management

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Real-time chat functionality

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User authentication and authorization

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Admin dashboard with analytics

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Modern, responsive UI with animations

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Advanced search and filtering

Technical Highlights

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Implemented cosine similarity algorithm for music recommendations

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Built microservices architecture with 4 independent services

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Real-time communication using WebSocket (Socket.io)

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RESTful API with Swagger documentation

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Docker containerization for easy deployment

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JWT-based authentication with role-based access control

Challenges and Solutions

Multi-service Communication

Designed RESTful APIs for service-to-service communication

Real-time Updates

Implemented WebSocket gateway for live chat

ML Integration

Created separate Flask service for recommendation engine

Technologies

Frontend

React Tailwind CSS Framer Motion Plotly.js

Backend

NestJS TypeScript MongoDB Socket.io

ML/AI

Flask Scikit-learn Pandas NumPy

Infrastructure

Docker Docker Compose

Authentication

JWT Firebase

Project Information

Status
Completed
Year
2024
Architecture
Microservices Architecture
Category
Machine Learning
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