SteveKrause.org: Pandora and Lastfm -- Nature [inherent qualities] vs. Nurture (as in, it's all about the people around you) in Music Recommenders:

Pandora is less subject to the echo chamber of overly like minds ["locked loops"], but it has its own fundamental challenge in its reliance on matching songs' "genes". Pandora's success hinges on a theory, and a specific implementation of that theory, about why music recommendations work. By contrast, Last.fm simply describes what goes together according to its audience and then makes relatively simple inferences from that. So if there are hidden factors that Pandora isn't explicitly capturing, Last.fm is at least capturing them indirectly.

It's not hard to find cases where Pandora's approach runs aground, although the system's lack of transparency makes it difficult to know where the problem lies. For example, it's hard to explain Pandora's initial choices for Gary Numan (he of "Cars" fame). With Numan as the seed, Pandora gave me syrupy pop tunes by Orchestral Maneuvers in the Dark and the Human League. Yes, each artist's most famous material was from the same time and was primarily electronic, but the latter two really miss the Numan aesthetic, which is more like supercooled liquid metal than warm syrup. Pandora went on to do somewhat better, but not great, with subsequent tunes. In comparison, Last.fm immediately delivered Numan-appropriate songs from Assemblage 23, Killing Joke, Kraftwerk, and Skinny Puppy, eventually drifting into less relevant territory. Still, Pandora partially redeemed itself with an inspired connection: "Out of Control" by Ric Ocasek (former leader of the Cars), an obscure cut from an artist that is far from obvious as a connection for Gary Numan. striking incidental connection that the above brief parentheticals for Ocasek & for Numan each say 'Cars' - to understand that this does not indicate an obvious connection, I needed to learn that Numan is famous for a song called "Cars" not for anything to do with the band that Ocasek headed.
I think I remember Colbert talking about Ric Ocasek - removing him, maybe from his dead-to-me board? and then Ocasek was there, on the show? maybe.

I found Last.fm better than Pandora at delivering songs that I liked or at least didn't feel compelled to skip, which is the most important thing when I'm listening while doing something else. Meanwhile, Pandora had more misses but was more likely to surface something truly out of left field, as with the Ric Ocasek example. I think Pandora has greater promise because it is far easier for Pandora to incorporate Last.fm's functionality than the other way around. That said, Pandora's advantage comes at a significant cost to its business, with all the manual work it entails. At this point, Pandora is not delivering proportionally more benefit for that cost—which is why I used the word "promise" above.

The key to Pandora's changing the game is to take better advantage of its exclusive, hard-to-replicate metadata about music. Users may never be able to objectively judge the quality of recommendations among different services, but they can definitely tell the difference between services with unique ways of getting to recommendations. For example, I'd like to see Pandora expose some of its internal attributes as dials for the user to control. If I put in the singer Paul Westerberg (former leader of the Replacements), I'd like to tell the system ie to match more strongly along his lyrical style right I'd like that too rather than by the fact he has a 'gravely male voice' (which is one of the things Pandora said it was matching on). It's easy to picture many other creative uses of Pandora's metadata, both in terms of a recommender and other applications.


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Finally, I wonder why Pandora continues to employ hundreds of attributes. In the world of modeling preferences, hundreds of variables typically can be consolidated down to a much smaller number with nearly the same predictive power. Typically, you start with a large number of variables as a kind of fishing expedition and then, over time, reduce the set down to those that are doing most of the work. The reduced set can be part of the original set and/or new variables derived specifically for predictive power. For a manual-labor-intensive business like Pandora's, being able to cut the number of variables in half (or a lot more) would help contain the costs. And if there's good reason not to consolidate attributes -
well aren't they interested in the Musical Genome Project in the first place, in its own right?

Pandora Internet Radio:
Ever since we started the Music Genome Project, our friends would ask:
Can you help me discover more music that I'll like?
Those questions often evolved into great conversations. Each friend told us their favorite artists and songs, explored the music we suggested, gave us feedback, and we in turn made new suggestions. Everybody started joking that we were now their personal DJs.
We created Pandora so that we can have that same kind of conversation with you.

see. Pandora was subsequent to the Music Genome Project.

On January 6, 2000 a group of musicians and music-loving technologists came together with the idea of creating the most comprehensive analysis of music ever.
It's not about what a band looks like, or what genre they supposedly belong to, or about who buys their records - it's about what each individual song sounds like.