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Random Alpha Pagerank (RAPr)


Stanford Reference:

08-085


Abstract


Stanford engineers have developed a patented algorithm that improves search results from ranking the objects of a database when viewed as a graph (e.g. a web graph). This system, Random Alpha Pagerank (RAPr), computes the importance of pages in a web graph using a random variable as a teleportation coefficient (compared to the standard PageRank algorithm which assumes a constant coefficient). This approach uncovers new characteristics that can be used to provide more relevant results for web searches, web spam detection, or gene/protein classification. It can also be used to derive more meaningful measures of importance by incorporating user behavior or domain specific knowledge.

Stage of Research
The inventors have completed a study that shows spam ranking with RAPr has a meaningful improvement in the performance of web-spam detection. They have also demonstrated that the model is valid for measured user behavior on the web.

Applications


  • Web searches - may reveal patterns in user behavior, which may suggest advertising strategies
  • Web spam detection
  • Gene/protein classification - may be useful in identifying genes that are sensitive to perturbations in the Markov model

Advantages


  • More relevant results - this model provides new features to uncover characteristics of a web graph that can be used to provide better search results
  • More capable modeling - the model is flexible and lets you incorporate either user behavior or “anti”-user behavior to investigate items that users will probably see or users will probably not see.

Publications



Innovators & Portfolio


  • Paul Constantine   
  • David Gleich   

Patent Status



Date Released

 5/4/2015
 

Licensing Contact


Imelda Oropeza, Licensing Associate
650-725-9039 (Business)
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[-] Map/Timeline

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Related Keywords


search algorithm   search tools   software: bioinformatics   internet: search tools   spam   internet search   ranking algorithm   search   internet security   recommendation system   security software   farm team   
 

   

  

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