Skip to Content

“Everything Is a Recommendation”

The next generation of online recommendation engines is less obvious, but more pervasive.
March 23, 2015

When Barneys New York launched a fashion line by the Oklahoma City Thunder basketball star Russell Westbrook last July, executives didn’t know exactly who would buy those clothes. They didn’t need to. The answers quickly emerged from an online shopping innovation that’s often overlooked: the recommendation engine.

Traditional versions of the technology are simple. Tell Netflix your feelings about a few movies and it suggests more. Read a product page at Amazon and it shows you similar alternatives. These are the tools that helped make those companies huge. But today, new technologies and much bigger arrays of available data are taking recommendation engines like the one Barneys uses to a new place, making them less obvious to the user but more important to website operations.

One example is how recommendations may show up as auto-completing search results. After a shopper at Jenson USA’s online bike shop enters the first two letters of a search for “full face helmet,” the recommendation system displays a list of helmets in an order based on that customer’s profile. At Neiman Marcus, each shopper’s online experience is similarly customized according to the person’s behavior on previous visits—and even in the current one.

Better tagging technologies let retailers dig more deeply into the specific design details of clothes. That’s how they can highlight new designers like ­Westbrook to appropriate customers, zeroing in on specific features of his designs. It’s similar to the way Pandora groups sound-alikes to build audiences for musicians.

When more sophisticated recommendation engines entice casual browsers with such tailored page selections, the chance they will buy something triples, says Matt Woolsey, executive vice president for digital at Barneys.

“The old way of making recommendations online is about catching up to the customer—you let them tell you about themselves and chase them,” says Woolsey. “We’re trying to use big data to get ahead of the customer.”

The technologies that make online recommendations as well-tailored as a Barneys suit are big-data software like Hadoop and the hardware to run it on, says Joelle Kaufman, chief marketing officer of BloomReach, a startup based in Mountain View, California, that is one of about three dozen vendors in the field.

Location-based data from mobile phones can play an important role, too. Other sources of consumer insight just beginning to inform these new engines’ recommendations can include purchase history from offline stores and social-media history.

A quick run through the Barneys site illustrates how it works. Woolsey and I each went to the menswear page and clicked on the same $150 watch. Since my limited browsing and purchase history focused on less expensive items, I got a list of watches ranging from $95 to $250 as counter-suggestions at the bottom of the screen. Woolsey, who acknowledged cheerfully that he probably dresses better than most reporters, was shown watches costing between $330 and $1,100.

Making this possible are parallel-­processing technologies that process massive amounts of data quickly, says BloomReach’s Kaufman. Emerging systems can propose dozens of different algorithms to choose the next page the consumer might see.

At Neiman Marcus, BloomReach’s technology can change what types of clothing appear on the womenswear page after just a few clicks. After Kaufman clicked on three sweaters, a tab for Jimmy Choo shoes disappeared, replaced by a gateway to sweaters on sale.

“That’s instantaneous machine learning,” she says. “Everything is a recommendation.”

Keep Reading

Most Popular

Rendering of Waterfront Toronto project
Rendering of Waterfront Toronto project

Toronto wants to kill the smart city forever

The city wants to get right what Sidewalk Labs got so wrong.

Muhammad bin Salman funds anti-aging research
Muhammad bin Salman funds anti-aging research

Saudi Arabia plans to spend $1 billion a year discovering treatments to slow aging

The oil kingdom fears that its population is aging at an accelerated rate and hopes to test drugs to reverse the problem. First up might be the diabetes drug metformin.

Yann LeCun
Yann LeCun

Yann LeCun has a bold new vision for the future of AI

One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.

images created by Google Imagen
images created by Google Imagen

The dark secret behind those cute AI-generated animal images

Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.

Stay connected

Illustration by Rose WongIllustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.