Next week in New York, Facebook is
"That's a very difficult problem at large scale, with so many ads and millions of people," said Greg Linden, founder of Findory and an architect of Amazon.com's early product recommendation and personalization engine. "And the data's not well tied to purchases like at Amazon, or even like, where so many Web searches are about products."
The predominant question on everyone's mind is: Can Facebook build anclever enough to keep pace with the passing fancies of its members? Facebook will have to get really good at processing all of the data it has collected on its reported 49 million members--demographics, personal preferences, and social histories--to predict what advertisements they might actually like and respond in their "news feed" or next to their "wall," according to industry executives.
That's no small task. In fact, it's a massive computing problem and one that very few companies apart from Google and Amazon have mastered. That's why Facebook--a company known for its young, fun culture--has been trying to hire more seasoned experts in so-called machine learning who can develop the right algorithms for a new generation of targeted advertising, people familiar with the company say.
One tech executive characterized the challenge like this: "The company that can process the most data will win." That maxim has proven true of Google in Web search and Amazon in e-commerce sales and product recommendations. Now Facebook must figure out how to take billions of data points about its members and turn that into an automatic ad machine.
A Facebook representative declined comment for the story.
There's no question Facebook is sitting on a data goldmine, with an exhaustive amount of information on people's preferences, backgrounds, and social histories--all given voluntarily by members. Facebook has profiles that include people's favorite music, television shows, books, and hobbies; their job history, education, birth date, and marital status; as well as daily activities, social networks, and interest groups. Traditional ad networks would kill for all that information in one place.
But with that data comes some interesting machine learning problems, experts say.
Machine learning is a broad term in the field of artificial intelligence. It refers to developing algorithms that can discover patterns in data and learn from them. Google, for example, has used probabilisticmodels to serve results to data searches based on keywords. With advertising, it's all about matching the right person to the right ad. And on an individual level, that's a tall order.
So some technologists focus on lumping people into groups or types, tracking their typical behaviors in aggregate, and then trying to predict what they might want or do next.
Machine learning in online advertising might involve trying many different techniques on affinity groups to figure out which work best. That's because no one obvious technique is the silver bullet for social networks--no one has solved the problem of serving ads in that setting before.
That's why Facebook must perfect a subtle product placement or recommendation system. To do it, it will have to invent algorithmic tricks. For example, knowing a list of people's friends isn't necessarily useful unless the system could automatically remind people of birthdays, and then advertise a specific gift the friend might like based on his or her preferences.
Aggregate Knowledge in Palo Alto, Calif., which is backed by Google investor Kleiner Perkins Caufield & Byers, may also have an ad solution for social networks. Aggregate has developed algorithms to determine what are called "affinity clusters" of people and, based on the personality profiles of those people, targets ads. It does this by looking at people's habits in aggregate, rather than as individuals.