Kev Price SEO

Saul Hansell has written a very revealing article called “Google keeps tweaking its search engine” for the New York times.

Mr Hansell had the privilege of spending the day with the search quality team and interviewing the ‘master of the google ranking algorithm’, Amit Singhal. Something many SEOs would give limbs for.
Mr Singhal’s name until now has not been on the radar (well not as far as I have noticed). I am hoping to hear a great deal more from him in the future as his interview confirmed many of the speculations that the SEO community have had recently.

The entire interview is a good read and well worth heading over to the NY Times for. For those of you who can’t be bothered with the whole thing however, here are some snippets:

Recently, a search for “French Revolution” returned too many sites about the recent French presidential election campaign — in which candidates opined on various policy revolutions — rather than the ouster of King Louis XVI. A search-engine tweak gave more weight to pages with phrases like “French Revolution” rather than pages that simply had both words.

and

Mr. Singhal introduced the freshness problem, explaining that simply changing formulas to display more new pages results in lower-quality searches much of the time. He then unveiled his team’s solution: a mathematical model that tries to determine when users want new information and when they don’t. (And yes, like all Google initiatives, it had a name: QDF, for “query deserves freshness.”)

Mr. Manber’s group questioned QDF’s formula and how it could be deployed. At the end of the meeting, Mr. Singhal said he expected to begin testing it on Google users in one of the company’s data centers within two weeks. An engineer wondered whether that was too ambitious.

“What do you take us for, slackers?” Mr. Singhal responded with a rebellious smile.

THE QDF solution revolves around determining whether a topic is “hot.” If news sites or blog posts are actively writing about a topic, the model figures that it is one for which users are more likely to want current information. The model also examines Google’s own stream of billions of search queries, which Mr. Singhal believes is an even better monitor of global enthusiasm about a particular subject.

The interview highlights the problems that Google are having with what they call ‘Freshness’. This is something that the SEO community has been discussing in great detail a long time prior to this article being published i.e. how to get new results listed.

The solution so far has been to do keyword and news research and find hot topics of the moment then write about them. This is using freshness to your advantage in that up to the minute news requires fresh results, the article seems to confirm this to be a good method.

The first quote I posted about the ‘French Revolution’ search I found interesting as it’s a good example of Google struggling with freshness. The search was bringing back fresh results because the election was a hot topic and recent news. Google’s solution was to tweak the algorithm specifically for that set of keywords, and the fact that they have the tools to do that and are willing to use them are revealing in themselves.

As Google compiles its index, it calculates a number it calls PageRank for each page it finds. This was the key invention of Google’s founders, Mr. Page and Sergey Brin. PageRank tallies how many times other sites link to a given page. Sites that are more popular, especially with sites that have high PageRanks themselves, are considered likely to be of higher quality.

Mr. Singhal has developed a far more elaborate system for ranking pages, which involves more than 200 types of information, or what Google calls “signals.” PageRank is but one signal. Some signals are on Web pages — like words, links, images and so on. Some are drawn from the history of how pages have changed over time. Some signals are data patterns uncovered in the trillions of searches that Google has handled over the years.

“The data we have is pushing the state of the art,” Mr. Singhal says. “We see all the links going to a page, how the content is changing on the page over time.”

Increasingly, Google is using signals that come from its history of what individual users have searched for in the past, in order to offer results that reflect each person’s interests. For example, a search for “dolphins” will return different results for a user who is a Miami football fan than for a user who is a marine biologist. This works only for users who sign into one of Google’s services, like Gmail

200 Signals? I’m pretty sure as an SEO community we could come up with about that many measurables