How Bounce Rate May Affect PageRank

By factoring in bounce rate data in calculation of PageRank values, Fabien Mathieu and Mohamed Bouklit create a new and enhanced ranking algorithm.

Back PageRank

In their research paper titled “The Effect of the Back Button in a Random Walk: Application for PageRank”, Mathieu and Bouklit (LIRMM) propose an enhancement to Google’s original algorithm. They factor in the bounce rate of a document and refer to it as either “Reversible” or “Irreversible Back”. By doing so they’re giving the traditional PageRank model an interesting and potentially useful addition, a possibility for the random surfer to “return” by hitting the back button.

This is a fundamental shift in the well-established PageRank model which has found its application in numerous areas already, even beyond search. The concept of  “backoff process” was first introduced by Fagin in “Random walks with back buttons” in 2000.

Mathieu and Bouklit take this a step further and apply their formulas to a collection containing 8 million documents which has produced results different to those of typical Google results. The research paper was published in 2004 and it is likely that their version of PageRank which offers better modelisation of the web users has been merged with a semantic pertinence-sort and tested out in a practical search situation.

We will attempt to contact the authors of this paper for their comments.


Mathieu F., Bouklit M,. 2004 – The Effect of the Back Button in a Random Walk: Application for PageRank

Dan Petrovic, the managing director of DEJAN, is Australia’s best-known name in the field of search engine optimisation. Dan is a web author, innovator and a highly regarded search industry event speaker.

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