As I walked through a windy, chilly Somerville on my way to visit music-discovery startup The Echo Nest yesterday, last weekend’s sudden shutdown at Matchmine weighed on my mind. After all, The Echo Nest is in the same general business as the now-defunct Needham, MA, company: building software that helps digital media companies provide Web users with personalized recommendations, by rating the intrinsic qualities of songs or other media files in the companies’ catalogs and matching them with the tastes expressed by users. And both companies were built around the belief that today’s dominant recommendation systems, based on a decade-old technique called collaborative filtering, do a lousy job of helping people discover new music and other media.
But after pouring nearly two years of work and $10 million in financing into its MatchKey service, Matchmine was unable to find enough clients to keep its main investor, the Kraft Group, from pulling the plug in a bid to help stem its own cash flow crisis. So I had lots of questions for the guys at The Echo Nest—who just brought their company out of stealth mode last month—about whether there’s a real demand for new music recommendation technologies.
As it turns out, they had some pretty good answers. While the company hasn’t said which media companies it’s working with, CEO Jim Lucchese says the startup has been approached by a long list of music-driven organizations—think social networking sites, Internet radio stations, and the like—that are racing to upgrade their recommendation services to compete with hot online music platforms like Pandora and Last.fm. “Recommendation has moved from a nice-to-have to a must-have,” Lucchese says. “There are a lot of companies that need to get into parity with their competitors.” The Echo Nest plans to start talking about some of its customers in a couple of months, Lucchese says, and by the end of the first quarter of 2009, he predicts, “we’ll be powering applications on a number of the comScore top 10 music properties,” referring to websites monitored by the audience measurement service comScore Inc.
An MIT spinoff founded in 2005 (nearly two years before Matchmine), The Echo Nest may also be benefiting from an early decision to stay small and raise little capital. That meant the company could afford to wait a little while as the Internet music economy picked up steam.
“I can’t speak directly to Matchmine’s experience, but there are companies in the recommendation space that over the last couple of years raised tens and tens of millions of dollars, and we did not,” Lucchese says. “Until recently, we were five people. For a company like Matchmine, part of the problem could be that they were at full ramp-up a little bit too early for the market.” But in the last couple of months the market has really increased, Lucchese adds: “We have a very long pipeline right now of deals where people are saying ‘We need to get this out by the first quarter.'”
So what exactly is this technology that The Echo Nest’s clients are in such a rush to implement?
The company’s story starts at the MIT Media Lab, where its co-founders and co-CTOs, Tristan Jehan and Brian Whitman, met as graduate students in Professor Barry Vercoe’s “Music, Mind, and Machine” group. Jehan was developing software that automatically picked out the tempo, rhythm, and other parameters in songs. Whitman was applying text retrieval techniques to information about music—and he says he was “pretty pissed off” about the state of the art in Web-based music recommendation services.
“Amazon and almost every large online store uses sales and clickstream data to do things like saying, ‘Okay, Jen bought these four records, and you bought three of them, and you don’t know Jen, but you should probably buy that fourth one as well,'” Whitman says. That’s collaborative filtering—and it was largely invented by Media Lab professor Pattie Maes (an Xconomist) and her students in the mid-1990s (her recommendation company Firefly Network was bought by Microsoft in 1998 for $40 million).
But while collaborative filtering “sounds like a great idea,” says Whitman, “when you apply it to music, stuff just gets lost.” Music with lower sales volume, and therefore less clickstream or sales data, will never show up in a collaborative-filtering-based search. So while collaborative filtering makes tracks and albums from popular bands even more popular, it marginalizes newer or edgier groups (a point I also made in my first profile of Matchmine).
Whitman was by no means the first to find collaborative filtering unsatisfying. Back in 2000, a composer and record producer named Tim Westergren had started the Music Genome Project, an effort by scores of trained musicologists to rate songs according to nearly 400 attributes such as melody, harmony, instrumentation, and rhythm. That led to the creation of Pandora, a free streaming music service that uses the attribute scores to help users discover new music by comparing it to the tunes they already like.
Pandora is brilliant, in my personal opinion. [Editor’s note: I’d have gone with “wicked awesome,” but the point’s a valid one nonetheless.] It offers free, 24/7 access to a very large catalog of music. The Pandora iPhone app, which makes all of that music available to mobile-device owners on the go, has brought the company hundreds of thousands of
