Music Recommendation System

This project presents an automated sound clustering method depending on machine learning and music information retrieval (MIR). Allowing people to search their favorite song or music and listen to the most similar ones throw a graph of songs, where each node represent a song.

WebApp Installation

Clone the repository

git clone https://github.com/aoso3/Music_Recommendation_System.git

Switch to the repo folder

cd Music_Recommendation_System

Install all the dependencies using composer

composer install

Copy the example env file and make the required configuration changes in the .env file

cp .env.example .env

Generate a new application key

php artisan key:generate

Generate a new JWT authentication secret key

php artisan jwt:generate

Refer to the link to install ElasticSearch

https://github.com/elastic/windows-installers/blob/master/README.md

Start the local development server

php artisan serve

You can now access the server at http://localhost:8000

Clustering Installation

Refer to the link to install SparkLib

https://www.knowledgehut.com/blog/big-data/how-to-install-apache-spark-on-windows

Insert the resulting clustering data from the clustering app to ElasticSearch database.