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Telling You What You Like

'Preference engines' track consumers' choices online and suggest other things to try. But do they broaden tastes or narrow them?

September 20, 2005|Alex Pham and Jon Healey | Times Staff Writers

Geoff Lester's two iPods can hold 20,000 songs, so he turned to the iTunes Music Store, which stocks 1.5 million online selections. But how to choose?

After the 28-year-old Los Angeles resident picked a few tracks, the software algorithms powering iTunes took over, popping out song after song they calculated that he might like.

He did. He estimates that he bought 500 tracks he otherwise wouldn't have over the last two years. He snapped up songs from the Dining Rooms, Zero 7 and Headset -- bands that aren't exactly mainstream.

"Before, you'd only find out about new music from whatever's playing on the radio," said Lester, an account manager for Nascar. "Now, you can find out about all kinds of bands. It's definitely opened up some genres for me that I would never have found any other way."

Time was, a trendy friend would offer advice on what to watch or listen to, where to stay or eat, what to wear or drive. But in the Digital Age -- when customers are a mouse click away from virtually everything -- not even the most plugged-in human can keep up with all the choices.

But computers can, and the growing ranks of so-called preference engines try to capture the intricacies of taste to help buyers navigate the plenty.

Like Coldplay? Check out Moby. Fan of Ian McEwan? Try Philip Roth. Those who bought "Harry Potter" also liked "The Chronicles of Narnia."

"We're just being flooded with content," said Erik Brynjolfsson, professor of management at the Massachusetts Institute of Technology Sloan School of Management. "And people are increasingly relying on recommenders to help them sort through it all."

Preference engines emerged in the earliest days of e-commerce to boost sales -- the Internet equivalent of "Would you like a belt to go with that?" -- but they have improved with technology and incorporated human feedback to more precisely predict what someone might like.

Their spread worries some who fear that preference engines can extract a social price. As consumers are exposed only to the types of things they're interested in, there's a danger that their tastes can narrow and that society may balkanize into groups with obscure interests.

"As these things get better and better, nobody has to encounter ideas they don't already agree with," said Barry Schwartz, professor of sociology at Swarthmore College and author of "The Paradox of Choice: Why More Is Less." "We lose that sense of community we had when there were shared cultural experiences, even though we may not have liked them. Now we can create our own cocoon and keep all that unpleasant stuff out."

The most common recommendation tools involve collaborative filtering, a technique that suggests products based on what other people with similar tastes have bought. These tools keep tabs on what people purchase, what items they browse or whether they put items into their shopping carts. Some take a further step by asking people how well they liked their purchases.

The idea is to divine clusters of taste, based on the actions of thousands of people, so that when a new person arrives, the website can start matching their taste against others in the database and begin making recommendations.

These systems boost sales. They also solve a problem created by the Internet -- the problem of too many choices. Consumers confronted with dozens or hundreds of choices generally have little difficulty making their selections. But in an online marketplace with thousands or millions of options, many freeze, unable to decide.

"It paralyzes people into indecision," Schwartz said.

Having such a wide selection also makes navigation unwieldy, particularly when the choices are obscure. The original site, which offered songs from unsigned artists and bands, exemplifies the challenge.

"It had just so much music on it, it became completely unnavigable," said Tim Westergren, chief technology officer of Pandora, a company working on a recommendation system for digital music. "After a while, it became, 'You're on So what? Who's going to find you?' "

Preference engines are useful because they bubble to the surface a selection of items that otherwise would be overlooked. At Rhapsody, a digital music service offered by RealNetworks Inc., 90% of its database of more than 1 million songs are played at least once every month, thanks in part to the service's preference system.

Researchers then wanted to see whether something as complex as taste -- for music, movies, books -- could be boiled down to a mathematical formula.

"Human taste is complex, and I'm not sure it can be accounted for," said Ken Goldberg, a UC Berkeley computer science professor who studied whether a computer program could recommend jokes. "This is one of the biggest challenges we face because preference engines are based on the idea that if we agree on three movies, we will agree on the fourth. But it often doesn't work that way."

Preference engines have encountered other problems, such as when people buy gifts.

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