Yes, in fact, there is accounting for taste

Web sites strive to cater to your likes

December 10, 2006|By Chris Emery | Chris Emery,Sun Reporter

Sometimes you find a video-store clerk who knows exactly what movie you'll like. Or a clerk in a music store who senses your taste in bands. Or a bookworm who can deliver one terrific novel after another from the shelves.

Marc Pickett wants to take luck out of that equation. And win a million dollars in the process.

Pickett, a doctoral student at the University of Maryland, Baltimore County, is trying to perfect a "recommender." That's a computer program designed to analyze your cinematic tastes and predict what movies you'll like.

Online retailers, including Netflix, and Apple's iTunes music store, put great stock in recommenders. They're critical tools in their efforts to establish long-term relationships with customers and sell more products - such as movies, books, album tracks and other goods.

The program is particularly critical for Netflix, an online mail-order movie-rental giant whose livelihood depends on keeping customers happy enough to pay $5.99 or more every month for the opportunity to watch its videos.

In October, the company offered a $1 million prize to anyone who could develop a program 10 percent more accurate than its current recommender, known as Cinematch. A chance at that chunk of change set thousands of programmers around the country, including Pickett, to work on the problem.

"Imagine that our Web site was a brick-and-mortar store," said James Bennett, Netflix's vice president of recommendation (yes, that's his title). "When people walk through the door, they see the DVDs rearrange themselves. The movies that might interest them fly onto the shelves and all the rest go to the back room."

Elsewhere, work on recommenders has led to prototypes of mood-sensing digital music players and a program that produces virtual, three-dimensional maps of customers' music collections.

Bennett said the company hopes the contest will ultimately enable the Netflix site to offer even more personalized lists of movies to customers.

But improving algorithms that dispense advice is no trivial task, according to computer scientists and mathematicians working on the problem.

One hurdle is simply having enough raw computing power to study people's likes and dislikes in the first place.

To make its predictions, Netflix's Cinematch churns through a billion movie ratings the company has collected from its customers over the years. The program clusters customers in groups based on how they rate movies on a five-star scale.

"The problem is to help you find soul mates," said John Riedl, a professor at the University of Minnesota who developed an early recommender in the 1990s. "It looks for people who felt the same way you did about some movies and makes suggestions based on what other movies they liked."

A fan of The Godfather, for example, might like Goodfellas, Raging Bull, Scarface, Taxi Driver and Platoon, based on the viewing preferences of other fans of the classic 1972 gangster epic.

At Netflix, Bennett said, Cinematch can predict a customer's opinion of a movie within a half a star, on average. To improve on that score, the company has released anonymous data on 100 million customer ratings so that Netflix Prize contestants can test their algorithms on real people.

"We're using four souped-up computers, but we can only feed bits of the data in at a time," said UMBC's Pickett, standing in an office cluttered with computer monitors, keyboards and robotic parts.

His computer screen displayed a map he created to visualize the movie preferences of thousands of customers. People who appear in proximity on the map, Pickett said, had similar taste in movies in the past and thus are likely to feel the same about movies in the future.

The UMBC team hopes to submit the results of its first trial by the end of the year. "Winning the $1 million would be nice," Pickett said, "but we're really after the bigger prize: artificial intelligence."

As of Thursday, the frontrunner among the 13,487 teams competing for the prize had improved on the accuracy of the Netflix algorithm by 6.11 percent.

But no matter how good recommenders get, the vicissitudes of mood and taste make it unlikely that these programs will ever approach perfection, experts said.

Consider the classic 1950 science fiction movie Plan 9 from Outer Space, by director Ed Wood.

"Some people consider it the worst movie ever made, so most sci-fi fans won't like it," Pickett said. "But film buffs might watch it to figure out what makes a movie really bad."

Matthew R. Kuhlke, co-founder of the movie Web site, said he knew something was wrong when his software suggested his mother rent the Adam Sandler movie Billy Madison. She wasn't amused by Sandler's brand of juvenile humor.

"It's one of my favorite movies, but she hated it," recalled Kuhlke, who lives in San Francisco. "I realized we needed to do some fine-tuning."

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