The old days when traditional gatekeepers programmed content for the mass-market are gone. Technology is shaking up the “chain of command” across popular culture. In the case of music, more and more consumers are looking at each other (friends, mavens, people they don’t even know…) to discover new songs and guide their tastes.

We have already talked about our approach for helping people discover things through other people’s tastes. Now we want to share further insight on how this process works.

Using OpenStrands (our public APIs), MyStrands’ researcher and PhD Candidate Justin Donaldson has developed a visualization technique (Artist Network Visualization) that shows in real-time how people correlate artists with other artists by listening to songs from their personal libraries. Strands of related artists will form from individual user’s listening histories. Eventually, these strands will connect, establishing the artist as a “hub” of shared musical information between the two users.

The visualization serves as a sort of primitive recommendation system as well. If you see an artist you recognize and enjoy, see who they’re connected to. You may just happen to see a new artist or track that you don’t recognize, and there’s a good chance that they’ll be worth a listen as well. MyStrands captures these opportunities in far more detail in our recommendation engine, enabling our community to explore artists, users, albums and many other items with equal ease. All these relationships form our “matrix of associations” which establishes a “ground truth” for how we at MyStrands understand the world of media content.

Try out Artist Network Visualization now!