This program is a network based recommendation system that evaluates artists and music on a variety of criteria, including song similarity and overall artist similarity. It does so by making comparisons between artists with information such as genre overlap, and takes a deeper dive into their songs to measure overlap in things such as mood or energy.
Maestro is one component of a project for GEICO Hacktivates 2019. My team created a chatbot focused on music recommendation & playback.
Alongside Trevor Cunningham, Aidan Miller, and Adam Ratzmann, I represented Indiana University - Bloomington against the University of Maryland - College Park in the semi-final round.
We will be representing IU again on November 8th against the University of California - Riverside in the final round. The full repository for the first leg of the team's project can be found over at Adam Ratzmann's Github.
My method for music recommendation is built by forming a network of artists, and then creating links between them with associated weights. The weights' values are the 'artist similarity score'.
This is determined by generating
- their shared genre score — measures the % of shared genres between artists
- top songs similarity score — measures the similarity of both artists top 10 songs
I've used Spotipy for acquiring music data from Spotify's API. In order to build networks of musicians with that data, I've used NetworkX.
You'll need to install the aforementioned packages, NetworkX and Spotipy
pip install networkx
pip install spotipy
Afterwards, you'll need to pop over to Spotify's Developer Dashboard and grab a Client ID and Client Secret.
Once you've done that, go ahead and create a file called apiKeys.py, and add:
spotifySecret = 'Your_Secret_Key'
Save the file.
In the spotifyData.py file, replace the client_id value in the credentials variable with your Client ID from Spotify.
Coming Soon!
Coming Soon!