Paul Sakuma, Associated Press
LOS GATOS, Calif. — Netflix executives John Ciancutti and Todd Yellin are trying to create a video-recommendation system that knows you better than an old friend. It's a critical mission as Netflix faces pressure from its Internet video rivals and subscribers still smarting from recent price hikes.
A big part of Netflix's future rides on how much Ciancutti, Yellin and about 150 engineers can improve the software that draws up lists of TV shows and movies that might appeal to each of the video-subscription service's 26 million customers.
Netflix has spent 13 years learning viewers' disparate tastes so it can point out movies they might enjoy. It has become good enough to figure out which romantic comedies might still appeal to subscribers who favor action flicks.
Netflix says three-quarters of what people watch now come from such recommendations. But as subscribers shift from getting DVDs through the mail to the instant gratification of Internet viewing, Netflix needs to make those suggestions even better.
The goal now is to learn individual viewing preferences so well that every recommendation is a hit with that subscriber, says Ciancutti, Netflix's vice president of product engineering.
If Ciancutti can get the system right, Netflix can direct people to movies and TV shows it already has. That will keep customers happy and help limit how much Netflix has to spend to obtain rights to additional online video.
If he gets it wrong, customers will be more inclined to search for something and become frustrated when they can't find it. That's a real concern because Netflix's online library doesn't offer as comprehensive a video selection as the DVD service the company wants to phase out.
"We are using all of our best ideas right now, but I know a year from now, I am going to be looking back and saying, 'Oh wow, we didn't have this feature or that feature,' and I will be really embarrassed," Ciancutti says.
Ciancutti invented Netflix's original recommendation system, which was mostly based on the ratings of customers willing to share their opinions about DVDs.
When he first started out, Ciancutti just wanted to come up with a system that didn't recommend DVDs in short supply or movies that a subscriber had already watched. The recommendations have become progressively more sophisticated as more people signed up for Netflix and engineers got more data to crunch and feed into its computer formulas.
But the company's engineers always realized they weren't getting a complete picture. Those discs may have sat around for weeks or might not have ever been watched in their entirety. And many customers have never bothered to rate movies.
Ciancutti believes the recommendations should get better now that more people are turning to online viewing. Since Netflix introduced its streaming option five years ago, billions of hours of video have been watched to give the company's engineers more insight into how and when customers use the service.
With streaming, Netflix no longer needs customers to give feedback. Its computers log whether someone mostly watches comedies on weekends and dramas after work, and whether the entire movie gets watched. It knows which customers tend to devour multiple episodes of TV comedies in a single viewing session. It can tell whether someone tends to rewind movies when certain actors appear.
"The signals we are getting about what people are watching, when they are watching and how much they are watching are much richer than ever before," says Neil Hunt, Netflix's chief product officer.
But the science remains imperfect.
Netflix subscriber Sanchita Gupta still gets confounded by some of the suggestions after eight years as a customer.
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