Listen to Local Music
There are great musicians all around us.
By listening to their music and supporting them in your community, you not only encourage the artists to make art, but you also strengthen the local economy by helping small businesses thrive.
Relaunching September 2023!
Name
City
Personalized Local Music Recommendations
Localify has been designed to help you discover local artists that are where you are. We connect the artists that you already love to local artists with a similar sound.
Automatically Generate Spotify Playlists
We automatically create personalized Spotify playlists for you. Listen to recommended local artists side-by-side with songs that you already enjoy.
Once you hear how good these local artists are, support them by buying merchandise, donating to their live streams, and seeing them in concert.
Why You Should Support Local Music
More Exposure
Spotify works to flood your daily playlists with the music that makes them the most money. When you listen to a local artist, you help them make it into more playlists which can have a domino affect on their streaming numbers.
Local Economy
When you go see a local show, you not only support the musicians, you support the entire community. You also support local businesses or entrepreneurs you interacted with on your night out. All of your commerce gets recirculated within the local economy creating a more vibrant and healthy community.
Quality of Life
A healthy music scene contributes to a high quality of life for everyone in the community. It provides artists an outlet for creative expression and encourages kids to learn how to play instruments. Most importantly, it has the power to connect people from all walks of life.
Developed by Students Supported by the National Science Foundation
Localify has been designed and developed primarily by students at Ithaca College and Cornell University. Due to the generous support of the National Science Foundation, it is free to use and will never be supported by ads. Localify also supports research related to machine learning, recommender systems, software engineering, and human-computer interaction.
