The Taste of Others

The Taste of Others

How can all these websites presume to know what you might "also like?" Scott Kirsner pulls back the curtain to reveal the technology of taste that drives these consumer sites.

If pressed, Joe Konstan will admit that he watches "fewer than two movies a year." Yet Konstan has spent more than a decade creating software that attempts to figure out your taste in movies and suggest what you might want to watch next.

Konstan, a professor at the University of Minnesota, is just one of dozens of academic researchers trying to solve the problem of understanding our fickle predilections. In the commercial sector, last fall Netflix offered a $1 million prize to anyone who could help the DVD rental company improve its movie recommendations by at least 10 percent. Amazon has been a pioneer in examining the DVDs you've purchased (and others that you've searched for) and making suggestions about what you may want to buy next. Several start-up companies, including ChoiceStream and Matchmine, are also trying to design software that better intuits your likes and dislikes.

MatchMine.com

MatchMine.com

It's a tall challenge, and recommendation software can still be clueless at times: just because I once bought a how-to-knit DVD for my wife as a gift, Amazon constantly suggests that I might want to own more craft-oriented discs. Netflix doesn't have a way for users to let it know that they never watched a given DVD, even though it sat atop their TV for three weeks, which seems like it'd be an important piece of information.

The systems all need information about you to make a guess about what you like. On e-commerce sites like Amazon, that tends to come from DVDs you've bought or looked at; on movie rental sits like Netflix or Blockbuster Online, users rate movies they've seen. On Netflix, the typical user has rated about 200 movies (I'm at 316), and one especially passionate user has rated about 90 percent of the site's 85,000 movies, according to spokesman Steve Swasey. (No one at Netflix is convinced that this person has actually seen all those movies.)

Swasey explains that one way to make Netflix's recommendations more accurate is to create multiple profiles within an account. On his account, he says, because he and his daughter have different profiles, her movie ratings stay separate from his, and they each get different recommendations.

Konstain says that one thing that can skew recommendations is when users only rate movies they like. "That means you don't have enough negative examples to train the system on what kind of recommendations would be bad to make," he says. So letting the system know about movies you despise can produce a better set of suggestions.

One problem that the technology hasn't yet begun to deal with is our changing moods. "You might enjoy one movie when you're alone and feeling down, but things are different when you're going to be with a bunch of people," Konstan says. There's no way yet to tell Blockbuster that you're feeling burnt out on a Friday night and need some light entertainment, or that you owe your girlfriend a "date night" movie, or that you're in a documentary frame-of-mind.

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