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.
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.
Join +60.000 others for monthly insights on SEO and artificial intelligence. Crafted by industry experts.
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?
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.
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:
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.
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:
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.
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.
As an SEO professional, there are several ways you can take advantage of Google's machine learning in content ranking to optimize your website:
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.
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.