Imagine someone searching for “Internal Yahoo Reorg Memos” (without the quotation marks) at Yahoo.com. You might end up with the following results:
Now imagine that the search engine might look at the links pointing to each of those pages (and maybe a few more of the pages in the results), and look at the anchor text used in links to those pages.
Yahoo could gather that different anchor text, and perform more searches. It might then take results from these new searches, and grab the anchor text from links pointing to the pages in the new search results. And so on. And so on.
Earlier this month, the ability to watch YouTube Videos In Google Earth was reported. The Google Maps User Guide also describes how videos can be added to Google Maps, and how those maps can be viewed in Google Earth.
A couple of new patent applications from Google look at how objects can be shared in Google Earth, how they might be ranked against each other, and how the very large size of the data base, holding different file types, might handle dynamic uploads of new information.
Google Earth indexing Challenges
In Google’s search results, depending upon your query, when and where you are searching, and what your browser and search engine settings might be, you may receive a different set of search results than other folks performing a search using the same query terms.
And those results may include a mix of links and images from different data sources including Web results, images, advertisments, local business, books, products, and others.
Google’s Universal Search provides a blended mix of results which incorporate results from a number of different data respositories all together into search results.
While ads are usually segmented from other results, the remainder may be mixed together upon results pages. David Bailey, on the Official Google Blog, provided a glimpse of how those results came to be blended together in Behind the scenes with universal search. He provided an even more detailed view in a guest post at Search Engine Land titled An Insider’s View Of Google Universal Search
In August 2007, the First International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD’07) was held so that participants could share their thoughts on how data mining and advertising interact, and address issues in this field.
One of the papers discussed during the workshop from Yahoo Research, was Pay-per-action model for online advertising, by Mohammad Mahdian and Kerem Tomak
The online advertising industry is currently based on two dominant business models: the pay-per-impression model and the pay-per-click model. With the growth of sponsored search during the last few years, there has been a move toward the pay-per-click model as it decreases the risk to small advertisers.
Changes in seasons can trigger changes in the amount of searches that people use for certain queries and topics.
For many websites owners, understanding those seasonal variations may lead to more visits from people who are interested what they offer upon their pages.
By seasons, I don’t just mean winter, spring, summer and fall. There are many different seasons that can affect how and what people search for, such as seasons for baseball, football, basketball, and hockey.
Or seasonal variations based upon recurring holidays such as Christmas, Valentines Day, Mothers Day, and Thanksgiving. The start of a school year can trigger certain searches, and summer break from school can impact other searches.
If you own a web site, how do you measure the way that people interact with your site? What data do you look at, how do you analyze it, and what do you do with that analysis?
The topic is becoming a popular one on the Web, and I have some links below to some articles on the subject that I thought were pretty interesting. I was inspired to collect those links after looking at a patent filing from Yahoo that describes some of the methods that they might use to try to understand how engaged people are upon their web properties.
The patent application is Techniques for measuring user engagement, and the listed inventors are Francesca M. Soito and Nitin Sharma (who appears to have now moved to Google).
User Engagement Variables
Does a search engine work better if it can figure out whether or not a search query is a name?
The folks at Ask.com appear to think so, and even want to know if the name is that of someone famous. I’m not sure how they measure fame, but they have a method for flagging names of the famous, as well as names that look like names, and names that really aren’t names (Brandy Alexander, anyone?)
The process is described in a patent application from Ask, and details how they might go about figuring out whether “Usher” or “50 Cent” or “Attila the Hun” refer to people, or to something else completely.
Systems and methods for predicting if a query is a name
Invented by Eric J. Glover, Apostolos Gerasoulis and Vadim Bich
US Patent Application 20070239735
Published October 11, 2007
Filed: April 5, 2006
At SIGIR 2007, one of the workshops held at the July Conference in Amsterdam was on Web Information Seeking and Interaction.
Web information seeking and interaction involves looking at the way that searchers interact with Web-based content and applications when they are looking for something. The conference covered a wide range of research, and I want to go into a little more detail on a couple of documents that were authored or co-authored by Google Employees.
The papers and working notes from the workshop contain a nice mix of topics, which are worth taking a look at. The papers at that link that initially caught my attention was one on experiments with eye tracking and mouse movements, and another that explored strategies for Web search.
Exploring How Mouse Movements Relate to Eye Movements on Web Search Results Pages
Kerry Rodden (Google) and Xin Fu (University of North Carolina, Chapel Hill)