Unless you spend hours a day on the Internet sifting through tons of music, finding the tracks you want can be tedious. Goombah harnesses the power of the community to find the music you want. Using distributed computing to analyze and match iTunes music libraries, Goombah finds each user’s "nearest neighbors" in music. iTunes music libraries are revealed to neighbors, and used to generate highly reliable music recommendations. That means that music fans who use iTunes can now find others who best match their tastes, wherever they are in the world. Users are automatically connected to online music stores where they can sample and purchase recommended music. And free music will soon be recommended to Goombah users with this same precision.
Goombah operates by automatically passing along recommendations based on the new tracks people play, not what is most marketed. The effect snowballs—the more a song is played, the more it is recommended, the more people hear it, and so on. As the Goombah community grows, our large-scale distributed-computing approach to recommendation has the potential to become the world's most precise music recommendation engine.
