How a Search Engine Might Rerank Search Results Based upon Time-Based Data in Query Logs

If you search at Yahoo for the phrase “world cup” (without the quotation marks), chances are good that the search engine will show you mostly pages about the 2010 World Cup, even though the tournament is held every 4 years and there may be many pages relevant for the phrase that don’t focus specifically upon a particular year.

How likely is it that when someone searches for “world cup,” they are looking for information about the upcoming tournament, taking place in South Africa between June 11th, and July 11th, 2010? On the other hand, how likely might it be that they want to find information about the world cup held in 2006? Or just general pages about the sporting event?

If I told you that the search engine was likely reordering those search results based upon time-based data, would it surprise you? Would you expect a Yahoo or Google or Bing to focus upon rerank search results in a manner like this, when they have some temporal aspect to them, such as a search for the Olympics, or the World Series, or the World Cup?

It’s quite possible that a search engine would look through its query logs, and see if a particular query is often included in more specific searches that include some kind of temporal data such as a year, or month, or day or time of day, and rewrite a searcher’s query to include that time-based information. A recent Yahoo patent application explains one fairly simple approach towards showing such information. The patent application is:

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How a Search Engine Might Identify Possible Query Suggestions

Like the information architects who organize the content on websites, search engine designers should aspire to provide users with scent at every step of their information-seeking process. Techniques like query suggestions, faceted search and results clustering all offer users the opportunity to make progress on their next step, rather than always having to restart the information-seeking process from scratch. Indeed, faceted search is a popular technique for offering users such guidance.

While users are ultimately responsible for expressing their information needs, it is the search engine’s job to act like a reference librarian and help the users in this process.

Reconsidering Relevance and Embracing Interaction
by Daniel Tunkelang

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Google Studies How Search Behavior Changes When Searchers Are Faced with Difficult Questions

A paper by Google researchers Anne Aula, Rehan M. Khan and Zhiwei Guan published last month asks the question How does Search Behavior Change as Search Becomes More Difficult? (pdf)

The paper describes two studies in which participants were given informational tasks to perform – a mix of hard and easy questions – to see if searchers adopted different strategies for searching when they were faced with questions where there were definite answers where answers to those questions might be difficult to find. An example of one of the difficult tasks (can you find the answer?):

You once heard that the Dave Matthews Band owns a studio in Virginia but you don’t know the name of it. The studio is located outside of Charlottesville and it’s in the mountains. What is the name of the studio?

The first study had 23 people performing searches, finding answers to questions like the one above, and examining the searches they performed and the pages they visited to see how they went about finding answers. The second study expanded to 179 searchers, and based some of the processes used on things they learned from the first experiment. A general conclusion from the second study:

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Google Word Completion and Search Query Suggestions from Social Network Connections?

When you type a query into a search box at Google or Yahoo or Bing on your desktop computer, chances are a drop down listing of suggested query terms will appear below the search box.

If you use a smart phone, and start typing into a text box on your phone, your phone may also offer you some suggestions to complete the word you are typing.

In the case of a cell phone where you need to press numbers to represent alphabetical characters, those suggestions can help save you from typing a lot of keystrokes. The phone offers terms from a dictionary stored on your phone to help you complete those terms.

A recent patent application from Google describes how they might add words to a dictionary like that, taken from social networks where you might be a member. What’s interesting about that is how much information the search engine captures about your use of words on the Web, and that of people whom you might be connected to on the Web.

Why might Google look to social network information for this kind of information?

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Google Defines Semantic Closeness as a Ranking Signal

This post may get you thinking about the benefits of using heading elements and lists on web pages for SEO purposes from a slightly different perspective than you may be used to.

Google uses a large number of signals to decide upon the order of pages shown in search results. Some of those signals measure the quality or importance of a web page, while others may indicate how relevant a page is for a particular search query entered into a search engine’s search box.

One fairly obvious relevancy signal is whether or not the words in a query actually appear upon a page that might be a search result for that query. If those words appear on the page more than once, the page might be considered even more relevant for that particular query than other web pages where the terms only appear once, or not at all.

Another factor that might indicate how relevant a page is for a particular set of terms is how close those terms might be on a page. While you could easily count the number of words between individual query terms to determine how close they are to each other, the formatting of web pages presents some challenges to the approach of simply counting words between terms, such as in a list like the following:

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Google’s Reasonable Surfer: How the Value of a Link May Differ Based upon Link and Document Features and User Data

Not every link from a page in a link-based ranking system is equal, and a search engine might look at a wide range of factors to determine how might weight each link on a page may pass along.

A diagram showing different values for links passing amongst three different web pages.

One of the signals used by Google to rank web pages looks at the links to and from those pages, to see which pages are linked to by others. Links from “important” pages carry more weight than links from less important pages. An important page under this system is one that is linked to by other important pages, or by a large number of less important pages, or a combination of the two. This signal is known as PageRank, and it is only one of a large number of Google ranking signals used to rank web pages and determine how highly those pages show up in search results in response to a query from a searcher.

An early paper by the founders of Google, The Anatomy of a Large-Scale Hypertextual Web Search Engine, tells us:

PageRank can be thought of as a model of user behavior. We assume there is a “random surfer” who is given a web page at random and keeps clicking on links, never hitting “back” but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank.

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What Makes a Good Seed Site for Search Engine Web Crawls?

Would search engines be better if they started web crawls from sites like Twitter or Facebook? Wikipedia or Mahalo? DMOZ or the Yahoo Directory?

The Web refreshes at an incredible rate, with new pages added, old pages removed, and words pouring out from blogs, news sites, and other genres of pages. Ecommerce sites showcase new products and eliminate old ones. New sites launch and old domains expire.

Search engines attempt to keep their indexes of the Web as fresh as possible, and send out crawling programs to find the new, update changes, and explore disappearances. Failure to do so means outdated search engines that deliver people to deleted pages, overwritten content, and stale indexes that miss out on new sites.

When a search engine starts crawling the Web, it often begins by following URLs from chosen seed sites to explore other pages and other domains. But how does a search engine choose those seed sites?

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New Reason to Submit Businesses to Google Maps: Google Navigator and Personal Information Management Integration?

If you have a business where you want customers to visit in person, and you haven’t added and/or verified that business in Google Maps, you may want to consider doing so. You can do this regardless of whether you have a web site or not.

The Google Navigator system that Google has developed for mobile phones allows people to navigate to destinations in their cars, and even search for types of nearby businesses rather than specific businesses at specific addresses. So, if you want to find a nearby Thai restaurant, you can type in “Thai restaurant” and Google will either show you the nearest one it knows about, or provide a list of restaurants that you can choose from.

A new patent application from Google hints at even more features from such a navigation system that can associate information from your personal information management software into the Google navigation system, from programs such as contact lists, calendars, and task lists.

For instance, you set up a task list on your smart phone to visit a new client, and then pick up stamps and mail out letters, drop off drycleaning, and go grocery shopping. You’ve also added the new client’s address to your personal information system contact list and calendar.

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