How good is Google’s Instant Mix?

You couldn't do a test with other collections because the beta is very limited right now-- I can only think of a few people I know that have access. But I can concur that my results are as terrible as his were.

Yes, he works for EN (which he's very clear about) and yes it's a bit inside baseball showing a service that most people can't use (because they're not developers or customers) but you can scroll past our results & take it as a post titled "How is Apple so much better than Google at such a data driven task?"

(I would have given the lack of beatles on genius -24 too! I also found a couple EN clunkers that I'd give a WTF to, but I'm a notorious jerk for those things, ask anyone i work with :)

While the author could have been more objective about his criteria, ironically enough, I think he missed the more salient point that he raised by implication: that music engines should be mapping the user's behavior patterns vis-a-vis the songs in his collection, and not so much objective connections between songs. This is what Genius does and has always tried to do, and it's why Genius seems to work better for the author. Genius doesn't focus as heavily on attempts to forge objective links between songs, so much as it focuses on attempts to draw links in behavior patterns w/r/t songs by likeminded users.

When listening to songs in a collection, our brain maps out its own connections between songs, as reflected in the way we compile our own lists consciously or subconsciously. Sometimes those connections make objective sense (i.e., "I want to listen to '70s funk, so I'm going to pick ten '70s funk songs in a row."). Sometimes those connections make little objective sense (i.e., "I am listening to a track by Lady Gaga, and afterward, I feel like listening to a track by J.S. Bach."). A good mixing engine figures out the idiosyncracies and subjectivity of our brains, as reflected statistically by the choices we've made in the past.

I think that if you survey the researchers in the field, the "WTF test" would be considered fairly reasonable - especially for a quick-and-dirty evaluation. Can you point to any specific songs that he said were WTF's and you think aren't, or vice/versa? If not then it would appear to meet the objectivity criteria.

Using his own music collection might be slightly more suspect. Changing that might have flipped the outcome of iTunes vs EchoNest, but wouldn't have changed the real news here: Google does really, really badly.

There's no objective evaluation of playlists. I've proposed a simple, subjective one that I think gets the job done. I'm happy to try other ones if you have something to suggest.

To what end? Unless Google is trying to make sure that when its software becomes sentient and takes over the world it has good taste in music (heh) I can't see much use for the data beyond building a recommender.

Let Google build their product first, it is still a beta, for a reason. Music recommendation is close to impossible without lots of data - and it becomes unfair to compare Google Music with iTunes Genius, which has been on the market in 3 years.