Your preferences are portable (sort of) with Matchmine

We last covered Matchmine in September 2007, noting that the product, a preferences and recommendation engine, was on to something very interesting. They were just going about it the wrong way. I'm glad to report that company has seen the error of its ways and, while keeping true to its mission, now has a product that makes sense.

To recap: Matchmine will tell you what media you will like (it covers blogs, music, video, and movies) based on what you tell it you already like. It's the same idea that you see every day in Netflix, except the Matchmine technology is more robust and accurate, according to CEO Mike Troiano. And, more importantly, the preferences data are portable.

Inside the Matchmine database: The key to getting you to rent more movies is knowing your specific tastes.

Here's how it works--or at least how it should work once Troiano gets the business fully up to speed: You tell your media site what you like, just as you do right now, and then the super-duper Matchmine algorithm calculates the "distance" between your items and other items in its data model to find new things for you that you'll like. Troiano says that even a small improvement in the preferences engine can have a dramatic impact on sales at a media site, and that Netflix, even with a prize offered for the solution, has not been able to improve its engine 10 percent in one year of trying. Matchmine does better, he says.

Individuals' Matchmine "keys" are stored on Matchmine's servers, not in a local file that requires an executable to access, as in the last, wacky version. This is good. It means that sites that want to participate in the Matchmine system simply have to strike a deal with the company to be able to offer up the cross-site recommender to their users. They don't have to get the users themselves to install anything.

It will be hard to get market-leading commerce sites (Amazon, Netflix, Last.fm, etc.) to adopt this product, since they no doubt see their users' preferences as strategic assets. However, if Matchmine can actually improve their recommendation success metrics, it may become more strategic for them to adopt the technology than to shun it.

The company is also hoping that consumers will continue to demand personal data portability. People don't want their preference data locked into commerce sites, Troiano maintains, even if the sites don't want to let it go. This system might just satisfy both sides of the market: It would give users a way to take their likes and dislikes with them, while at the same time making them less inclined to do so, since the sites that use Matchmine would offer a better experience because of their deeper understanding of customers' preferences.

Because of the strategic change Matchmine needs to effect in its potential customers, it will be a difficult company to grow. But difficult is good--it means it won't have dozens of me-too companies hustling for the same business.

You can try Matchmine on the music site Fuzz and the film site FilmCrave.

[Supergeek007]

Great, next time I need a recommendation for a good book to read or movie to watch I can use matchmine. The problem is i have no friends or family to blame if the selection is bad. Yella

[fapap]

I'm working on the same idea but for restaurants, hotels, bars (just about any type of point of interest). Were in the process of mining any data source we can to start building some correlations between places, but need user's rating to be as accurate as we want. If anyone is interested in seeing how were attacking it check out http:///www.myfriendsuggests.com (we are open to all comments or ideas).