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Facebook predicts what you like to see on web page

The conventional wisdom of HTTP web page request between browser and servers is to transmit the response as whole in the form of a structured mark up language. However, this may not be how it works in today’s social networks.

Today’s smart social networks like Facebook use Hadoop and Hive driven intelligence to predict which resources of web page have a predetermined likelihood to be included in a response to a future request. Resources which includes java-scripts, style sheets, image etc are identified based on map-reduce and other computational algorithms which run on distributed systems analyzing billions of entries in the resource logs. These identified resources are stored/cached in server hash maps and the page is rendered in phase wise manner.

This method and system is described in US patent 8,108,377 Predictive resource identification and phased delivery of structured documents (Inventors: Jiang; Changhao, Wei; Xiaoliang; Assignee: Facebook, Inc. (Palo Alto, CA)) .

As the disclosure goes on to describe Hadoop and Hive usage, we find that
“…the resource logging, analyzing, filtering, predicting, and/or selecting operations discussed above can be implemented using Hive to accomplish ad hoc querying, summarization and data analysis, as well as using as incorporating statistical modules by embedding mapper and reducer scripts, such as Python or Perl scripts that implement a statistical algorithm. Other development platforms that can leverage Hadoop or other Map-Reduce execution engines can be used as well…”

Must say, this is one of the smart implementations of predictive computation which is reducing latency, limiting network load and overall leading to a better user experience. Remember, since this is patented, check with assignee before any commercial usage. 


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