A small page. A small page is quick to download and generally faster for your browser to display. This results in a minimalist design aesthetic; extra fanciness in the interface slows down the page without giving you much benefit.



A small page is quick to download and generally faster for your browser to display. This results in a minimalist design aesthetic; extra fanciness in the interface slows down the page without giving you much benefit. Complex algorithms with a simple presentation. Many search features require a great deal of algorithmic complexity and a vast amount of data analysis to make them work well. The trick is to hide all that complexity behind a clean, intuitive user interface. Spelling correction, snippets, sitelinks and query refinements are examples of features that require sophisticated algorithms and are constantly improving. From the user's point of view search, almost invisibly, just works better.

Many search features require a great deal of algorithmic complexity and a vast amount of data analysis to make them work well. The trick is to hide all that complexity behind a clean, intuitive user interface. Spelling correction, snippets, sitelinks and query refinements are examples of features that require sophisticated algorithms and are constantly improving. From the user's point of view search, almost invisibly, just works better. Features that work everywhere . Features must be designed such that the algorithms and presentation can be adapted to work in all languages and countries. Consider the problem of spell correction in Chinese, where user queries are often not broken up into words or Hebrew/Arabic, where text is written right to left (interestingly, this is believed to be an example of first-mover disadvantage -- when chiseling on stone, it is easier to hold the hammer in your right hand!).



. Features must be designed such that the algorithms and presentation can be adapted to work in all languages and countries. Consider the problem of spell correction in Chinese, where user queries are often not broken up into words or Hebrew/Arabic, where text is written right to left (interestingly, this is believed to be an example of first-mover disadvantage -- when chiseling on stone, it is easier to hold the hammer in your right hand!). Data driven decisions - experiment, experiment, experiment. We try to verify that we've done the right thing by running experiments. Designs that may seem promising may end up testing poorly.

There are inherent tensions here. For instance, showing you more text (or images) for every result may enable you to better pick out the best result. But a result page that has too much information takes longer to download and longer to visually process. So every piece of information that we add to the result page has to be carefully considered to ensure that the benefit to the user outweighs the cost of dealing with that additional information. This is true of every part of the search experience, from typing in a query, to scanning results, to further exploration.





to her, has become the poster child example for

). We do a huge amount of analysis of the billions of pages on the web and our query logs to determine what are "real words" on the web, and what are likely to be misspellings. The system that gives you the spell correction has to, in a fraction of a second, consider a huge number of possible words you

have meant (vastly greater than any dictionary ever manually constructed) and determine if there is a more likely query you meant to type. When we are confident that you actually meant to type something else, we take a rare liberty with our search results: we try to distract you from looking at the top result on the page. The spelling correction is in your line of sight and colored a bright must-see red. Furthermore, we now make sure that

, unless it is as important to you as spelling! (so far, nothing is). The algorithms involved in spell correction are constantly getting better. They now work in a large number of languages and are even better at detecting when you have made a spelling mistake. Getting the spelling of your query right is so important that we are considering showing you the results of the spell-corrected query in the middle of the page (just in case you missed our bright red text at the top and bottom!





Having formulated your query correctly, the next task is to pick a page from the result list. For each result, we present the title and

, and a brief two line snippet

. Pages that don't have a proper title are often ignored by users. One of the bigger recent changes has been to extract titles for pages that don't specify an HTML title -- yet a title on the page is clearly right there, staring at you. To "see" that title that the author of the page intended, we analyze the HTML of the page to determine the title that the author probably meant. This makes it far more likely that you will not ignore a page for want of a good title. Below the title comes the snippet, and a key early innovation was in what Google showed for the snippet. At the time, search engines showed you the first two lines of the web page; Google, instead, showed you parts of the page where your actual search keywords showed up (information retrieval experts call this "keywords-in-context"). Showing keywords-in-context is visually simple and virtually indistinguishable from the simpler style of snippets, but vastly more useful in helping you decide which page to visit. This simplicity belies underlying complexity: when we create a snippet we have to go through the actual text from each result to find the most relevant part (which contain your keywords) rather than just giving you the first few lines.





We have been making improvements to our snippets over time with algorithms for determining the relevance of portions of the page. The changes range from the subtle -- we highlight synonyms of your query terms in the results -- to more obvious. Here' s an example screenshot where the user searched for "arod" and you can see that Alex and Rodriguez are

in the search result snippet, based on our analysis that you might plausibly be referring to him:



