Music Recommender System

An artist recommendation engine, from feeding Spotify playlists through word2vec

A Spotify song recommendation engine built with the power of graph analytics.

A Music Player with a Clustering based Recommendation Engine utilizing Spotify API

Building a recommendation engine using data available via the Spotify API.

A simple song recommendation engine using K-Nearest Neighbors with the Spotify Music Dataset

This repo contains jupyter notebook file for my blog about Spotify Recommendation Engine in Section.IO

Emotion Based Music Recommendation System : This project implements a Song Recommendation Engine using K-Nearest Neighbors with the Spotify Music Dataset. It combines data from Spotify artists, albums, and tracks to create a comprehensive dataset for analysis and recommendation.

A music search engine with the following that allows users to query an artist/song. 1) Wikipedia excerpt 2) Spotify Playlist 3) Twitter tweets about artist 4) Recommendation System

Designed and implemented a Spotify ETL pipeline using Airflow, MinIO, Postgres, and Metabase. Built a recommendation engine on top of structured data to suggest new tracks, demonstrating skills in data orchestration, data warehousing, and analytics engineering.

«Recommended Frequencies» is a recommendation engine for playlists and currently works for Spotify. Given a selected playlist in a user's library, the app suggests songs from the user's liked songs that may make a good addition to it.

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Fine-tune Spotify's recommendations to fit your style.

A Spotify Recommendation Engine

Use Spotify API to create robust and flexible recommendation system with visualizations.

Demonstration of implementation of the similarity based recommendation engine performed on spotify songs.

Intelligently Seeding the Spotify Recommendation Engine

Github Learning Lab Course: Building a Song Recommendation Engine with Azure (and Spotify API): a 20 step course with a final project of using cloud computing to create your own app based around specific boundaries.

Graph-based music recommendation engine using NetworkX & Spotify data

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

Spotify-Scale Music Recommendation Engine (Parallel + GPU Optimized) A high-performance music recommendation engine built with matrix factorization and collaborative filtering, optimized using OpenMP, MPI, and CUDA.

To build a music recommendation engine using Spotify Million Playlist Data (30 GB) and host its API on cloud.

A recommendation system (also commonly referred to as a recommendation/recommender engine/platform) seeks to predict a user’s interest in available items (songs on Spotify, for example) and give recommendations accordingly. There are two primary types of recommendation systems

Web Application that emulates the UI of Spotify, integrating a robust recommender system that utilize the audio features of selected songs to provide personalized recommendations and Analytical Engine capable of analyzing songs & genres.

Develop an application to curate new Spotify playlists with machine learning methods in Python

This learning lab course is part of Bit Project's 8 Week Program. By the end of the course, students will deploy a song recommendation web app using Azure Static Web Apps.

WIP A recommendation engine build on the spotify API using React+Redux

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Flexible content-based music recommendations using Spotify

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