"The more visits [to] 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," the paper's authors noted. The goal is to "leverage hundreds of millions of users' 'implicit voting' on page importance," they said, "in accordance with the concept of Web 2.0."

Missing the Mark

Google's trademarked PageRank method measures the relative importance of Web pages through the use of a sequence of data -processing instructions -- called a link analysis algorithm -- that assigns a numerical weighting to each element within any given set of hyperlinked documents.

"Pages that we believe are important pages receive a higher PageRank and are more likely to appear at the top of the search results," Google said. "We have always taken a pragmatic approach to help improve search quality and create useful products, and our technology uses the collective intelligence of the Web to determine a page's importance."

Gauging the relevance of Internet searches is extremely important to Google, Yahoo and Microsoft because it allows the search engine leaders to more precisely target their placement of ads on behalf of clients. But Microsoft and its collaborators claim that PageRank misses the mark because it allows the importance of pages to become artificially inflated.

For example, Web sites such as Adobe.com are ranked very high by PageRank because Adobe.com has millions of sites linking to it for Acrobat Reader and Flash Player downloads. "However, Web users do not really visit such Web sites very frequently, and they should not be regarded [as] more important than the Web sites on which users spend much more time, like MySpace.com and Facebook.com," they explained.

Giving Users a Vote

Microsoft and its academic collaborators say their new method is superior because it is based on a user-browsing graph that is generated from data that reflects actual human behavior. "User-behavior data can be recorded by Internet browsers at Web clients and collected at a Web server ," they said.

BrowseRank's user-browsing graph can more precisely represent the Web surfer's random walk process, and thus is more useful for calculating page importance, the collaborators claim. Furthermore, the amount of time spent on the pages by users is also included under the BrowseRank method.

"In this way, we can leverage hundreds of millions of users' implicit voting on page importance," researchers explained. "Experimental results show that BrowseRank indeed outperforms the baseline methods, such as PageRank and TrustRank, in several tasks."

For its part, Google notes that PageRank, which is based on a Stanford University patent, is not the only method it employs to rank search engine results. Instead, Google said it relies on more than 200 different signals to examine the entire link structure of the Web and determine which pages are most important.

"We then conduct hypertext-matching analysis to determine which pages are relevant to the specific search being conducted," Google explained. "By combining overall importance and query-specific relevance, we're able to put the most relevant and reliable results first."