Every day Google answers more than one billion questions from people around the globe
in 181 countries
and 146 languages. 15% of the searches we see everyday we’ve never seen before.
Technology makes this possible because we can create computing programs, called
“algorithms”, that can handle the immense volume and breadth of search requests. We’re
just at the beginning of what’s possible, and we are constantly looking to find better
solutions. We have more engineers working on search today than at any time in the past.
Search relies on human ingenuity, persistence and hard work. Just as an automobile engineer designs an engine with good torque, fuel efficiency, road noise and other qualities – Google’s search engineers design algorithms to return timely, high-quality, on-topic, answers to people’s questions.
For every search query performed on Google, whether it’s [hotels
in Tulsa] or [New
York Yankees scores], there are thousands, if not millions of web pages with
helpful information. Our challenge in search is to return only the most relevant
results at the top of the page, sparing people from combing through the less relevant
results below. Not every website can come out at the top of the page, or even appear on
the first page of our search results.
Today our algorithms rely on more than 200 unique signals, some of which you’d expect, like how often the search terms occur on the webpage, if they appear in the title or whether synonyms of the search terms occur on the page. Google has invented many innovations in search to improve the answers you find. The first and most well known is PageRank, named for Larry Page (Google’s co-founder and CEO). PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.
Google is constantly working to improve search. We take a data-driven approach and
employ analysts, researchers and statisticians to evaluate search quality on a
full-time basis. Changes to our algorithms undergo extensive quality evaluation before
being released.
A typical algorithmic change begins as an idea from one of our engineers. We then implement that idea on a test version of Google and generate before and after results pages. We typically present these before and after results pages to “raters,” people who are trained to evaluate search quality. Assuming the feedback is positive, we may run what’s called a “live experiment” where we try out the updated algorithm on a very small percentage of Google users, so we can see data on how people seem to be interacting with the new results. For example, do searchers click the new result #1 more often? If so, that’s generally a good sign. Despite all the work we put into our evaluations, the process is so efficient at this point that in 2010 alone we ran:
Search relies on human ingenuity, persistence and hard work. Just as an automobile engineer designs an engine with good torque, fuel efficiency, road noise and other qualities – Google’s search engineers design algorithms to return timely, high-quality, on-topic, answers to people’s questions.
Algorithms Rank Relevant Results Higher
For every search query performed on Google, whether it’s [hotels
in Tulsa] or [New
York Yankees scores], there are thousands, if not millions of web pages with
helpful information. Our challenge in search is to return only the most relevant
results at the top of the page, sparing people from combing through the less relevant
results below. Not every website can come out at the top of the page, or even appear on
the first page of our search results.Today our algorithms rely on more than 200 unique signals, some of which you’d expect, like how often the search terms occur on the webpage, if they appear in the title or whether synonyms of the search terms occur on the page. Google has invented many innovations in search to improve the answers you find. The first and most well known is PageRank, named for Larry Page (Google’s co-founder and CEO). PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites.
Panda: Helping People Find More High-Quality Sites
To give you an example of the changes we make, recently we launched a pretty big algorithmic improvement to our ranking—a change that noticeably impacts 11.8% of Google searches. This change came to be known as “Panda,” and while it’s one of hundreds of changes we make in a given year, it illustrates some of the problems we tackle in search. The Panda update was designed to improve the user experience by catching and demoting low-quality sites that did not provide useful original content or otherwise add much value. At the same time, it provided better rankings for high-quality sites—sites with original content and information such as research, in-depth reports, thoughtful analysis and so on.Testing and Evaluation
Google is constantly working to improve search. We take a data-driven approach and
employ analysts, researchers and statisticians to evaluate search quality on a
full-time basis. Changes to our algorithms undergo extensive quality evaluation before
being released.
A typical algorithmic change begins as an idea from one of our engineers. We then implement that idea on a test version of Google and generate before and after results pages. We typically present these before and after results pages to “raters,” people who are trained to evaluate search quality. Assuming the feedback is positive, we may run what’s called a “live experiment” where we try out the updated algorithm on a very small percentage of Google users, so we can see data on how people seem to be interacting with the new results. For example, do searchers click the new result #1 more often? If so, that’s generally a good sign. Despite all the work we put into our evaluations, the process is so efficient at this point that in 2010 alone we ran:
- 13,311 precision evaluations: To test whether potential algorithm changes had a positive or negative impact on the precision of our results
- 8,157 side-by-side experiments: Where we show a set of raters two different pages of results and ask them to evaluate which ones are better
- 2,800 click evaluations: To see how a small sample (typically less than 1% of our users) respond to a change
Manual Control and the Human Element
In very limited cases, manual controls are necessary to improve the user experience:- Security Concerns: We take aggressive manual action to protect people from security threats online, including malware and viruses. This includes removing pages from our index (including pages with credit card numbers and other personal information that can compromise security), putting up interstitial warning pages and adding notices to our results page to indicate that, “this site may harm your computer.”
- Legal Issues: We will also manually intervene in our search results for legal reasons, for example to remove child sexual-abuse content (child pornography) or copyright infringing material (when notified through valid legal process such as a DMCA takedown request in the United States).
- Exception Lists: Like the vast majority of search engines, in some cases our algorithms falsely identify sites and we sometimes make limited exceptions to improve our search quality. For example, our SafeSearch algorithms are designed to protect kids from sexual content online. When one of these algorithms mistakenly catches websites, such as essex.edu, we can make manual exceptions to prevent these sites from being classified as pornography.
- Spam: Google and other search engines publish and enforce guidelines to prevent unscrupulous actors from trying to game their way to the top of the results. For example, our guidelines state that websites should not repeat the same keyword over and over again on the page, a technique known as “keyword stuffing.” While we use many automated ways of detecting these behaviors, we also take manual action to remove spam.
Fighting Spam
Ever since there have been search engines, there have been people dedicated to tricking their way to the top of the results page. Common tactics include:- Cloaking: In this practice a website shows different information to search engine crawlers than users. For example, a spammer might put the words “Sony Television” on his site in white text on a white background, even though the page is actually an advertisement for Viagra.
- Keyword Stuffing: In this practice a website packs a page full of keywords over and over again to try and get a search engine to think the page is especially relevant for that topic. Long ago, this could mean simply repeating a phrase like “tax preparation advice” hundreds of times at the bottom of a site selling used cars, but today spammers have gotten more sophisticated.
- Paid Links: In this practice one website pays another website to link to his site in hopes it will improve rankings based on PageRank. PageRank looks at links to try and determine the authoritativeness of a site.
