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.
"We do a contest of algorithms in each context...and see which works best (to serve an ad) and that's a traditional machine learning technique. But this requires massive computing power," said Paul Martino, founder of Aggregate Knowledge. Martino said his company is in talks with various social networks, but does not yet have a deal with any of them.
One of the techniques in this field is known as collaborative filtering, which Amazon used when creating its product recommendation system. Amazon's system automatically analyzes your purchase history and looks for the same buying patterns among other shoppers. By sizing up the purchase histories of similar shoppers, the system can look for the products you haven't bought yet and that other similar shoppers have. Then it can suggest items you might also like.
Facebook plans to adopt similar techniques, for people who like the same music or films, according to people familiar with the company. That way, movie or music studios could "suggest" entertainment in the form of a product placement.
Another approach is to tailor ads to a person's demonstrated behaviors, or what's called behavioral targeted advertising. That means that a site might keep track of a person over time and factor in his or her demographics and preferences. A high-income woman who has recently said she's looking for a car might receive a Lexus ad, for example.
Facebook has already come up with some machine learning tricks to allow people to search on a person's nickname, even if the person hasn't divulged that information. According to one source, the company developed an algorithm that could strip words from people's "wall" (where friends post messages) and then remove from the list all the words that weren't in the dictionary. With what remained it built a comprehensive dictionary of nicknames so that people can search on a friend's profile under alternative names.
"That's an interesting machine learning problem, and they have a million things like that," said the source.
Facebook is already clustering people into groups, such as tech geeks or music lovers, and following patterns of people's behaviors to predict other kinds of behaviors, according to people familiar with the company. For example, groups who like baseball could like sushi, in a hypothetical link within data patterns that you might not expect. The ad network could then target ads for local sushi restaurants to members of that group. But the company hasn't deployed this methodology on a wide scale.
Facebook is also a tremendous barometer for public opinion. So the site could eventually track chatter about a movie like Spider-Man, for example, and sell that information to the film company or watch how a message about the movie on a wall is received. If the chatter falls off quickly, it could be time for the studio to release the movie on DVD rather than keep it in theaters.
The big search sites have done some work in this area. Google, for example, in order to send the right ad, looks at search terms, a person's physical location (by IP address), and type of content on the site he or she visited. Yahoo targets ads similarly, but it also sells behaviorally targeted ads to marketers. For example, Yahoo might deliver an ad for baby formula to a person the company know has looked at pregnancy-health pages.
Microsoft serves ads based on audience segment, such as car shoppers, by anonymously tracking the behavior of users across its network. If an MSN user has browsed MSN Autos or searched for "Kelly Blue Book" on Live Search, the user will see relevant ads. The search and Web browsing history data is blended with data people offer to Microsoft during registration, such as age and gender, but not identifiable information.
For now, Microsoft will be serving Facebook's graphical ads, but it remains to be seen how the social network will tackle the ad issue on its own.
"The problem isn't that they can't make revenue, it's that the expectation is so high on the amount of revenue that they can make," Linden said. "Because people aren't in a purchasing frame of mind (at Facebook), it's going to be hard for them to get as much from the advertising as the hype right now."CNET News.com's Elinor Mills contributed to this report.