Rapid music discovery engine powered by machine learning.
- Scrum Master: Sasha Bayan
- Engineering Lead: Eric Benson
- Project Manager: Zachary Lopez
We all love music, but it’s hard to find new songs we enjoy. Introducing Quentin, your personalized music Q-rator who continuously learns your preferences and finds Rad music you will enjoy.
Existing music players play full songs and focus on genre as a major criterion for recommendations, making it difficult to quickly discover music.
Our app uses cutting-edge Javascript frameworks as well as Echonest and Spotify APIs to address this market need.
We named our project Q-Rad.io: Q for Quentin, your future songs queue manager; Rad.io since its a Rad radio.
var RadSlice = FullSong.slice(30seconds);
After users log in, a queue of upcoming songs is generated based on the users’ prior interaction with the app.
Users are presented with a RadSlice of a song and can rate the radness of the song to continually re-calibrate the machine learning algorithm.
Based on user feedback, the upcoming songs are dynamically reloaded to reflect the user’s preferences.
If at any moment the user really likes a song they may listen to the full version on Spotify.
Javascript, Node.js, Express, MongoDB, Mongoose.js, React, Flux, Passport.js, Brain.js, Web Workers, Bluebird, HTML, CSS, Less, Gulp, Karma, Mocha, Chai, Casper.js, Jsx-test, Travis.CI, AWS
Running the following commands installs the dependencies and fires up the server at http://localhost:8000
npm install
bower install
gulpSee contributing.md for contribution guidelines.