Luke Barrington, a Music Information Retrieval researcher at UCSD, is trying to improve the state of the art in automatic playlist generation. He’s conducting a survey and he needs your help.
Image may be NSFW.
Clik here to view.
If you are interested in helping out, take the survey.
Here are the details from Luke:
With music similarity sites like Pandora.com or iTunes’ Genius feature that recommends playlists, based on a song that we like, our MIR domain of music similarity and recommendation is finding a mass audience. But are these systems any good? Could we make something better?
This is what I’m trying to figure out and I would like to include your opinion in my analysis.
We are conducting an experiment where you can listen to playlists that are recommended, based on a “seed song”, and evaluate these recommendations. We are comparing different recommendation systems, including Genius, artist similarity and tag-based similarity. Most importantly, we’re are trying to discover the important factors that go into creating and evalutating a playlist.
If you’d like to participate in the experiment by listening to and evaluating some playlists, please go to:
http://theremin.ucsd.edu/playlist/
As an incentive, we’re offering a $20 iTunes gift card to whoever rates the most playlists (but it’s about quality, not quantity!)
To learn more, ask questions or make suggestions, feel free to drop me a line.
Thanks for your help,
Thanks for your help,
Luke Barrington,
Computer Audition Laboratory
U.C. San Diego
van.ucsd.edu
Image may be NSFW.
Clik here to view.
Image may be NSFW.Clik here to view.
