How results are automatically generated
With the vast amount of information available, finding what you need would be nearly impossible without some help sorting through it. Google’s ranking systems are designed to do just that: sort through hundreds of billions of webpages and other content in our Search index to present the most relevant, useful results in a fraction of a second.
A Search bar with the query "hair salon near me"
Key factors in your results
To give you the most useful information, Search algorithms look at many factors and signals, including the words of your query, relevance and usability of pages, expertise of sources, and your location and settings. The weight applied to each factor varies depending on the nature of your query. For example, the freshness of the content plays a bigger role in answering queries about current news topics than it does about dictionary definitions.
Learn more below about the key factors that help determine which results are returned for your query:
Meaning of your query

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.

A query with

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.

Relevance of content

Next, our systems analyze the content to assess whether it contains information that might be relevant to what you are looking for.

The most basic signal that information is relevant is when content contains the same keywords as your search query. For example, with webpages, if those keywords appear on the page, or if they appear in the headings or body of the text, the information might be more relevant.

A website being inspected with a magnifying glass

Beyond looking at keywords, our systems also analyze if content is relevant to a query in other ways. We also use aggregated and anonymized interaction data to assess whether search results are relevant to queries. We transform that data into signals that help our machine-learned systems better estimate relevance. Just think: when you search for “dogs”, you likely don’t want a page with the word “dogs” on it hundreds of times. With that in mind, algorithms assess if a page contains other relevant content beyond the keyword “dogs” — such as pictures of dogs, videos, or even a list of breeds.

It’s important to note that, while our systems do look for these kind of quantifiable signals to assess relevance, they are not designed to analyze subjective concepts such as the viewpoint or political leaning of a page’s content.

Quality of content

After identifying relevant content, our systems aim to prioritize those that seem most helpful. To do this, they identify signals that can help determine which content demonstrates expertise, authoritativeness, and trustworthiness.

For example, one of several factors we use to help determine this is understanding if other prominent websites link or refer to the content. This has often proven to be a good sign that the information is well trusted. Aggregated feedback from our Search quality evaluation process is used to further refine how our systems discern the quality of information.

A website being analyzed by an algorithm

Content on the web and the broader information ecosystem is constantly changing, and we continuously measure and assess the quality of our systems to ensure that we’re achieving the right balance of information relevance and authoritativeness to maintain your trust in the results you see.
Usability of webpages

Our systems also consider the usability of content. When all things are relatively equal, content that people will find more accessible may perform better.

For example, our systems would look at page experience aspects, such as if content is mobile-friendly, so that those on mobile devices can easily view it. Similarly, they look to see if content loads quickly, also important to mobile users.

A website with a gold badge

Context and settings

Information such as your location, past Search history, and Search settings all help us to ensure your results are what is most useful and relevant for you in that moment.

We use your country and location to deliver content relevant for your area. For instance, if you’re in Chicago and you search “football”, Google will most likely show you results about American football and the Chicago Bears first. Whereas if you search “football” in London, Google will show results about soccer and the Premier League. Search settings are also an important indicator of which results you’re likely to find useful, such as if you set a preferred language or opted in to SafeSearch (a tool that helps filter out explicit results).

In some instances, we may also use your recent Search activity to present more relevant results. For instance, if you search for “Barcelona” and recently searched for “Barcelona vs Arsenal”, that could be an important clue that you want information about the football club, not the city.

Two websites showing a football and an American Football

Search also includes some features that personalize results based on the activity in your Google account. For example, if you search for “events near me” Google may tailor some recommendations to event categories we think you may be interested in.

These systems are designed to match your interests, but they are not designed to infer sensitive characteristics like your race, religion, or political party.

You can control what Search activity is used to improve your Search experience, including adjusting what data is saved to your Google account, at To disable Search personalization based on activity in your account, turn off Web & App Activity.

You can also find content preferences like SafeSearch in settings. These help you make a choice about whether search results include graphic content that may be shocking for some users.