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 stopped 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 factorsEdit
In 2010, a simplified version of the EdgeRank algorithm was presented as:
- is user affinity.
- is how the content is weighted.
- 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.
EdgeRank and its successors have a broad impact on what users actually see out of what they ostensibly follow: for instance, the selection can produce a filter bubble (if users are exposed to updates which confirm their opinions etc.) or alter people's mood (if users are shown a disproportionate amount of positive or negative updates).
As a consequence, for pages it may be nearly impossible to reach any significant audience without paying to promote their content.
- PageRank, the ranking algorithm used by Google's search engine
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