The user browsing graph can more precisely represent the web surfer's random walk process, and thus is more useful for calculating page importance. The more visits of the page made by users and the longer time periods spent by the users on the page, the more likely the page is important ... We can leverage hundreds of millions of users' implicit voting on page importance.One issue that came up, in discussions afterward about the paper, is whether BrowseRank gets something much different than a smoothed version of visit counts.
Some websites like adobe.com are ranked very high by PageRank ... [because] Adobe.com has millions of inlinks for Acrobat Reader and Flash Player downloads. However, web users do not really visit such websites very frequently and they should not be regarded [as] more important than the websites on which users spend much more time (like myspace.com and facebook.com).
BrowseRank can successfully push many spam websites into the tail buckets, and the number of spam websites in the top buckets in BrowseRank is smaller than PageRank or TrustRank.
Experimental results show that BrowseRank indeed outperforms the baseline methods such as PageRank and TrustRank in several tasks.
Let's say all the transition probabilities between web pages are set by how people actually move across the web, all the starting points on the web are set by where people actually start, and then you simulate random walks. If all this is done correctly, your random walkers should move like people do on the web and visit where they visit on the web. So, after all that simulating, it seems like what you get should be quite close to visit counts.
This is a bit of an oversimplification. BrowseRank uses time spent on a page in addition to visit counts and its Markov model of user behavior ends up smoothing the raw visit count data. Nevertheless, the paper does not compare BrowseRank to a simple ranking based on visit counts, so the question still appears to be open.
Please see also my earlier post, "Google Toolbar data and the actual surfer model".
Please see also my earlier post, "Ranking using Indiana University's user traffic", which discusses a WSDM 2008 paper that looked at supplementing PageRank with traffic data.