EdgeRank

EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed. As of 2011, Facebook has switched from using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account.

EdgeRank was developed and implemented by Serkan Piantino.

Formula and factors

In 2010, a simplified version of the EdgeRank algorithm was presented as:

$\sum_{\mathrm{edges\,}e} u_e w_e d_e$

where:

$u_e$ is user affinity.

$w_e$ is how the content is weighted.

$d_e$ is a time-based decay parameter.

• User Affinity: The User Affinity part of the algorithm in Facebook's EdgeRank looks at the relationship and proximity of the user and the content (post/status update).
• Content Weight: What action was taken by the user on the content.
• Time-Based Decay Parameter: New or old. Newer posts tend to hold a higher place than older posts.

Some of the methods that Facebook uses to adjust the parameters are proprietary and not available to the public.

See also

• PageRank, the ranking algorithm used by Google's search engine

External links

• edgerank.net
• Facebook - How News Feed Works