AI Case Study

Facebook improves user experience using ranking algorithm to optimise content based on user behaviour

Facebook uses machine learning techniques including ranking algorithms to select and order the items that show up in a user's newsfeed. This is done through ranking and prediction about how likely a user is to interact with it in certain ways. Presumably the more accurate the machine learning method used, the more stick Facebook becomes to its users.

Industry

Technology

Internet Services Consumer

Project Overview

Facebook's "ultimate goal would be to show people all the posts that really matter to them and none of the ones that don’t. They knew that might mean sacrificing some short-term engagement—and maybe revenue—in the name of user satisfaction.

Crucial as the feed quality panel has become to Facebook’s algorithm, the company has grown increasingly aware that no single source of data can tell it everything. It has responded by developing a sort of checks-and-balances system in which every news feed tweak must undergo a battery of tests among different types of audiences, and be judged on a variety of different metrics... To speak of Facebook’s news feed algorithm in the singular, then, can be misleading. It isn’t just that the algorithm is really a collection of hundreds of smaller algorithms solving the smaller problems that make up the larger problem of what stories to show people. It’s that, thanks to all the tests and holdout groups, there are more than a dozen different versions of that master algorithm running in the world at any given time... Facebook has spent seven years working on improving its ranking algorithm".

Reported Results

The engagement metrics for users interacting with content improved: "Facebook’s news feed ranking team believes the change in its approach is paying off. 'As we continue to improve news feed based on what people tell us, we are seeing that we’re getting better at ranking people’s news feeds; our ranking is getting closer to how people would rank stories in their feeds themselves' ".

Technology

Ranking algorithm(s); details unspecified.

Function

R And D

Product Development

Background

"Facebook—which was originally little more than a massive compendium of profile pages and groups, something like Myspace—built the news feed in that year [2006] as a hub for updates about your friends’ activities on the site. Users bristled at the idea that their status updates, profile picture changes, and flirtatious notes on one another’s pages would be blasted into the feeds of all of their friends, but Facebook pressed on.

Even then, not everything your friends did made it into your news feed. To avoid overwhelming people with hundreds of updates every day, Facebook built a crude algorithm to filter them based on how likely they were to be of interest. With no real way to measure that—the like button came three years later—the company’s engineers simply made assumptions based on their own intuition. Early criteria for inclusion of a post in your news feed included how recent it was and how many of your friends it mentioned."

Benefits

Data

User behaviour data as well as specific focus and test groups.