How Google deals with language and location

When you think of geo-targeting for SEO, what springs to mind? If you said country and language go body slam something, you’re the next lucky contestant on ‘Guess that Algo’. If you said type in co-ordinates and send the intercontinental up their arses… Well, methinks it’s time for some anger management.  Oooo..look, butterfly..

Anyway, an interesting patent was awarded to the Google and it’s worth having a quick look at. The patent in question is;

Ordering of search results based on language and/or country of the search results – filed October 21, 2008 and awarded February 26 2009 - Gupta; Vineet; (Bangalore, IN) ; Gomes; Ben; (Mountain View, CA) ; Lamping; John; (Los Altos, CA) ; McGrath; Mizuki; (Minato-ku, JP) ; Singhal; Amitabh; (Palo Alto, CA) ; Tong; Simon; (Mountain View, CA)

And it has some author relations with;

System and method for providing preferred country biasing of search results – filed June 27, 2003 and awarded November 11, 2008
System and method for providing preferred language ordering of search results – filed April 3, 2003 and awarded November 11, 2008

Essentially what we have here looks like the two separate components and the new parent connector. This essentially works by

1. Getting the search results (via existing ranking mechanisms)
2. Sorting/re-ranking of results based on language/country
3. Re-ranking of results based on region/language
4. Delivery of new re-ordered results set

Ok, so what are the considerations you ask? And what does any of it have to do with Klingons? We’re getting to that….

A journey into Google’s patent on generating suggestions

Search engines are always looking to make our lives easier, or at least accessing the world’s information in a timely manner. But in the old days they had to wait for the user to take action before they could begin to deliver potential results for a query – not these days – starting to gather search results and even implementing search assist can happen with each keystroke.

You know the one, the suggestions they make as you’re typing in a given query? It looks something like this;

I know more than a few SEO peeps have talked about this as a potential problem for some long tail targets and others have pondered if it would make a good keyword research tool. They can also use the same systems for query analysis as far as which documents to return and rank. But what if there is a potential for personalization of this data? Because there just might be; and that would certainly limit its overall effectiveness from a SEO perspective - At least as a research tool.

(Note; the following is a guest post from my good friend Miss CJ)

How search engineers look for nepotistic links

Paid links are a bit of problem for search engine engineers because they can be misleading.  Some links are bought in order to boost rank and others are purchased for legitimate reasons, such as actually offering something interesting to a website visitor.  Not all links should be discounted but different weights can be given to allow less important links to get a full "vote". 

It's not easy to differentiate between these, but there has been a fair bit of research around it we can look at.  If search engines could discount misleading nepotistic links, their performance would improve.  In the SEO community, this would be received with mixed emotions not doubt.  Google uses methods to detect keyword spamming for example, and uses other text based methods, but their algorithm is open to link spam.

But what are search engineers doing to combat nepotistic links in modern information retrieval?

Putting behavioural metrics in perspective

Wise men don't judge: they seek to understand. - (Wei Wu Wei)

So here’s the question; are behavioural metrics being used in modern search? You do remember them right? Those warm and fuzzy little signals such as bounce rates that there all the rage in late 2008 in the search engine optimization world? Sure you do… but let’s take one last look.

Although bounce rates received the biggest attention, we would be remiss not to start by quickly listing some signals commonly looked at by information retrieval folks. The two elements include implicit and explicit data (actions and interactions) – examples can include;

Implicit signals

1. Query history (search history)
2. SERP interaction (revisions, selections and bounce rates)
3. User document behaviour (time on page/site, scrolling behaviour);
4. Surfing habits (frequency and time of day)
5. Interactions with advertising
6. Demographic and geographic
7. Data from different application (application focus – IM, email, reader);
8. and closing a window.

Explicit signals

1. Adding to favourites
2. Voting (a la Search Wiki or toolbar)
3. Printing of page
4. Emailing a page to a friend (from site)

Now that we’re past that let’s get a little geeky so those information retrievers don’t shake their heads to hard at us – the terminology. I am as guilty as the next Gypsy of flinging the term ‘behavioural metrics’ about over the last year or so, even performance metrics. If you want to research this more, start by using the term; implicit/explicit user feedback signals – because that’s what we’re talking about.

and thanks to Steve Gerencser for sending the pic