Implement a content-based and collaborative filtering recommendation systems for song recommendations.

Content Based Music Recommendation Service

A Music Recommendation System Based on Sound Content

The goal for this project is to create an LLM based music recommendation system. This project is currently in its very early stages, however the goal of this project is to create an extremely flexible music recommendation system using a chat focused LLM on the frontend to interact with a robust recommendation system on the backend.

The Music Mood Lifter is a software system empowered by machine learning algorithms that can detect facial expressions from input faces.

Intelligence Where You'll Get Your Next Music. Industrial Training Project 2019. under the guidance of Ardent Computech Pvt. Ltd.

This Project is a music recommender based on the spotify's music database

Music recommender using Flask, PostgreSQL and the Spotify API

A simple music recommendation app that uses Flet for UI and an API using the Spotify Web API

This repository contains a web application that integrates with a music recommendation system, which leverages a dataset of 3,415 audio files, each lasting thirty seconds, utilising a Locality-Sensitive Hashing (LSH) implementation to determine rhythmic similarity, as part of an assignment for the Fundamental of Big Data Analytics (DS2004) course.

Emotion based music recommender system

Design and implementation from scratch of different models for a musical recommendation system

a Node.js API that interacts with the Spotify API to fetch user data, currently playing track, top tracks, and other Spotify functionalities.

This project summarizes the basic steps required to implement a basic recommendation engines that suggests new bands to users. Data are fetched from the open dataset of ListenBrainz in Bigquery. The recommendation engine is built by hacking the keras embedding layers to perform matrix factorization.

Implementing a music recommender with decision tree.

The `MKGCN` class, coupled with the Spotify API, orchestrates a multi-modal knowledge graph convolutional network to enhance music recommendation systems by integrating user interaction data and diverse music modalities.

An application that recommends music on the basis of previous heard songs of a user using a ML model. Using Collaborative-based filtering to recommend other songs similar to what the user likes. Download Data set from Kaggle (Million song data set)

The laboratory from Algorithmic Machine Learning Course at EURECOM

This project creates Music Recommendation Engine in Python using Spotify API and Machine Learning.