Scott Fish

Microsoft thinks that they have the answer to PageRank, by releasing a new concept called BrowseRank.  From the researcher’s paper about BrowseRank…

We propose computing page importance by using a ’user browsing graph’ created from user behavior data. In this graph, vertices represent pages and directed edges represent transitions between pages in the users’ web browsing history. Furthermore, the lengths of staying time spent on the pages by users are also included. The user browsing graph is more reliable than the link graph for inferring page importance.

“… The more visits of the page made by the users and the longer time periods spent by the users on the page, the more likely the page is important.”

- I would think that google doesnt need to shore up this piece, they give Google Analytics away for free and they already have these metrics.

Everyone’s Buzzing about it…

Microsoft’s BrowseRank Aims for Better Results than PageRank
Microsoft Goes After Google’s PageRank With BrowseRank

BrowseRank is worse than PageRank

Can BrowseRank best PageRank?
Microsoft tries to one-up Google PageRank
Microsoft dreams up a new kind of search with BrowseRank

ABSTRACT
This paper proposes a new method for computing page importance,
referred to as BrowseRank. The conventional approach to compute
page importance is to exploit the link graph of the web and
to build a model based on that graph. For instance, PageRank is
such an algorithm, which employs a discrete-time Markov process
as the model. Unfortunately, the link graph might be incomplete
and inaccurate with respect to data for determining page importance,
because links can be easily added and deleted by web content
creators. In this paper, we propose computing page importance
by using a ’user browsing graph’ created from user behavior
data. In this graph, vertices represent pages and directed edges
represent transitions between pages in the users’ web browsing history.
Furthermore, the lengths of staying time spent on the pages
by users are also included. The user browsing graph is more reliable
than the link graph for inferring page importance. This paper
further proposes using the continuous-time Markov process on
the user browsing graph as a model and computing the stationary
probability distribution of the process as page importance. An e-
cient algorithm for this computation has also been devised. In this
way, we can leverage hundreds of millions of users’ implicit voting
on page importance. Experimental results show that BrowseRank
indeed outperforms the baseline methods such as PageRank and
TrustRank in several tasks.