To return relevant results, we first need to establish what you’re looking for ー the intent behind your query. To do this, we build language models to try to decipher how the relatively few words you enter into the search box match up to the most useful content available.
This involves steps as seemingly simple as recognizing and correcting spelling mistakes, and extends to trying to our sophisticated synonym system that allows us to find relevant documents even if they don't contain the exact words you used. For example, you might have searched for "change laptop brightness" but the manufacturer has written "adjust laptop brightness. Our systems understand the words and intend are related and so connect you with the right content. This system took over five years to develop and significantly improves results in over 30% of searches across languages.
Our systems also try to understand what type of information you are looking for. If you used words in your query like “cooking” or “pictures,” our systems figure out that showing recipes or images may best match your intent. If you search in French, most results displayed will be in that language, as it’s likely you want. Our systems can also recognize many queries have a local intent, so that when you search for “pizza,” you get results about nearby businesses that deliver.
If you search for trending keywords, our systems understand that up-to-date information might be more useful than older pages. This means that when you’re searching for sports scores, company earnings or anything related that's especially new, you’ll see the latest information.