Google's Machine Learning in Content Ranking (Beginner's Guide)

Gain insights into how Google leverages machine learning in web page ranking. Learn about scoring models, vector space models, and the use of Markov chains.

Written by
Daniel Højris Bæk
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April 24, 2024

The world of search engine optimization (SEO) and how websites rank on search engines like Google can seem quite complicated.

But what if I told you that understanding a little bit about how Google uses machine learning can significantly improve your SEO game?

The Basics of Ranking

Ranking is essentially how search engines, such as Google, arrange and display web pages based on their relevance to a user's search query.

In Googles own words they "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."

Think of it like this: when you search for "best pizza places near me," Google will present a list of web pages. This arrangement is done based on relevance, and this is what we refer to as "ranking".

In different areas, this kind of sorting happens too, not just in search engines. For instance, when you're on a shopping website, the site might recommend products based on what you've bought in the past, or travel agencies might suggest hotel rooms based on your preferences.

How Does Google Decide This Relevance?

Here's where machine learning, a subset of artificial intelligence, comes into play. Without diving too deep into technical details, imagine machine learning as a method where computers learn from data, just as humans learn from experience.

To determine the relevance of a web page, Google uses a "scoring model". Think of it as a judge in a talent show, giving scores to each contestant. In our case, the contestant is the web page, and the talent is how relevant that page is to your search query.

Google uses various techniques for this:

  • Vector Space Models: It converts the content of the page and your search query into vectors (imagine them as points in space), and then checks how close or far these vectors are. The closer they are, the higher the relevance.
  • Learning to Rank: This is more advanced. Google's machine learns from past data and optimizes itself to predict a better score for each web page. This is the method we're focusing on here.

How Does Google Measure the Quality of its Ranking?

Just ranking the pages isn't enough. Google also needs to ensure that the pages it ranks higher are indeed of higher relevance. For this, it uses metrics like:

Mean Average Precision (MAP): Think of this as checking if the "talented contestants" are indeed talented.

Discounted Cumulative Gain (DCG): This is slightly complex but imagine it as giving more importance to contestants who perform well in the beginning of the show than at the end.

Getting Deeper: Machine Learning for Ranking

When Google uses machine learning for ranking, it looks at the search query, the webpage, and aims to predict how relevant the page is for that query. It then sorts or "ranks" these pages based on these predicted scores.

There are three main ways Google's machine does this learning:

  • Pointwise Methods: It tries to predict the exact score of relevance for a single page. It's like asking, "On a scale of 1 to 10, how good was the performance?"
  • Pairwise Methods: Instead of giving a score, it compares two pages and tries to predict which one is more relevant. It's like asking, "Who performed better, contestant A or B?"
  • Listwise Methods: This is the most direct approach. The machine tries to learn and predict the entire list of rankings in one go, much like ranking all the contestants in a talent show at once.

Markov Chains and PageRank

In addition to these techniques, Google also incorporates other predictive modeling concepts, such as Markov Chains which Googles original PageRank was also based on, to further enhance the accuracy of its ranking algorithms.

A Markov chain is a mathematical system that hops from one state to another. It's like a game of hopscotch, but where the next square you jump to is somewhat random, yet determined by certain probabilities. Importantly, your next jump depends only on your current square, and not how you got there.

Imagine the internet as a massive web of interconnected pages. Some pages link to others, creating this vast network. Now, think of a random surfer who starts on one webpage and then clicks on a link to go to another page, and so on.

The PageRank algorithm, in essence, tried to figure out how likely it is for this random surfer to land on any given page. Pages that are more likely to be landed on have a higher PageRank.

Google's use of Markov chains (via PageRank) was revolutionary because it shifted the focus from the content of the page alone to the structure and quality of the entire web.

Pages that were deemed important, because many other pages linked to them (and especially if those linking pages were important themselves), got higher ranks.

An illustration simplifying the Pagerank algorithm: Percentages reflect importance, and arrows represent hyperlinks.

Over the years, Google's ranking algorithms have grown much more sophisticated, and while PageRank is still a component, it's just one of many factors and ranking systems Google uses to rank webpages.


One key advancement in Google's ranking algorithm was RankBrain, an artificial intelligence-based system that plays a crucial role in understanding and interpreting search queries.

Introduced in 2015, RankBrain utilizes machine learning techniques to comprehend the meaning behind complex and ambiguous queries, making search results more relevant than ever.

While traditional algorithms primarily relied on matching the exact keywords in search queries with the content on web pages, RankBrain takes a step further.

It focuses on understanding user intent by analyzing patterns and connections between different queries.

This innovative approach enables Google to provide more accurate search results, particularly for queries it encounters for the first time.

RankBrain continuously learns from vast amounts of search data, adapting and improving its understanding over time.

By deploying a neural network model, this algorithm can process and interpret complex language patterns, allowing it to better comprehend user queries and serve relevant content.

Just like the Markov Chain and PageRank, it's important to note that RankBrain is just one of the hundreds of factors that contribute to Google's ranking process.

How to utilize the understanding of machine learning as an SEO

As an SEO professional, there are several ways you can take advantage of Google's machine learning in content ranking to optimize your website:

  1. Focus on relevance: Understanding that relevance is key, make sure your website and content align with the intent of the user's search query. Conduct thorough keyword research to identify the most relevant keywords and incorporate them naturally throughout your content.
  2. Create high-quality content: Google's machine learning algorithms are designed to identify and rank high-quality content. Focus on creating comprehensive, well-researched, and original content that provides value to your target audience. Avoid keyword stuffing or using thin content, as these practices can negatively impact your rankings. Remember, this does not mean you cannot use AI. Just focus on outputting quality content.
  3. Optimize for user experience: User experience plays a crucial role in content ranking. Ensure that your website is mobile-friendly, loads quickly, and provides a seamless browsing experience. Make your content easily readable by using clear headings, bullet points, and relevant images or videos.
  4. Pay attention to user engagement signals: Google's machine learning algorithms take into account user engagement signals, such as click-through rates, bounce rates, and time spent on page. Optimize your meta titles and descriptions to increase click-through rates, and create engaging and compelling content that encourages users to stay on your website.
  5. Stay up to date with algorithm changes: Google's algorithms are constantly evolving, and machine learning plays a significant role in these changes. Stay informed about algorithm updates, as they can impact your website's rankings. Keep up with industry news, attend conferences or webinars, and follow trusted SEO resources to stay ahead of the curve and know what to focus on - and what not!


The way Google ranks pages is a blend of several machine learning techniques and methods. For SEO professionals, understanding this isn't about diving deep into the technicalities but about grasping the idea that Google's goal, through machine learning, is always to improve user experience by providing the most relevant search results.

So, the next time you're optimizing a website or content for SEO, remember that relevance is key. And with the help of machine learning, Google is getting better and better at finding and promoting it.

Google's Machine Learning in Content Ranking (Beginner's Guide)

This is an article written by:

+20 years of experience from various digital agencies. Passionate about AI (artificial intelligence) and the superpowers it can unlock. I had my first experience with SEO back in 2001, working at a Danish web agency.