As its name suggests, Machine learning (or automatic learning) is a functionality that helps the software to perform a task without programming or explicit rules. This is one of the concepts that you need to understand to improve the traffic on your website. Keep reading What Is Google Machine Learning? to improve your performance.
RankBrain and BERT are the most famous machine learning upgrades. RankBrain is a machine-learning AI system that Google uses to help rank its search results. BERT is a technique based on neural networks for natural language processing (NLP) and is the acronym for Bidirectional Encoder Representations from Transformers.
They are used to understand the nuances and context of words within searches and help provide better matches to queries in the form of more relevant results.
Google has thousands of quality raters -quality evaluators- that are used to help improve the search experience.
According to Google, “These raters can be found anywhere in the world and are highly skilled professionals.”
Google is always experimenting with search results, and they use feedback from third-party search quality raters to make sure the changes are helpful.
So what are quality raters looking for when they analyze the results?
Quality rater guidelines
Quality raters are considered “highly qualified” because they are expected to follow a very long and detailed document, 168 pages from January 2020 to be exact.
They explain what constitutes a good result and what evaluators should look for to identify poor results.
The document is worth reading because it establishes what content Google wants to serve to its users and clarifies how Google judges the quality of content as “adequate”.
It doesn’t tell us what the ranking factors/signals are or how the algorithm works exactly (more on that later).
These guidelines are updated frequently. Here’s a brief overview of what Google raters look for to identify quality content (and thus, this is an overview of what you should aim to achieve).
By looking at the results, testers focus on the user’s intent, and the problem they are looking to find a solution for. So they ask: “is this result a good solution and helps the user?” If Google recommends a particular piece of content as a solution, that content must provide value to the user who is seeking a solution to their problem. The content must be user-centric and user-focused.
Credibility signals (known as E-A-T)
Google uses the acronym E-A-T – Expertise, Authoritativeness and Trustworthiness which could also be defined as credibility.
They are judging credibility or E-A-T on three levels:
- The page.
- The author.
- The website.
Additionally, they try to see if the content is believable in the context of the solution it purports to provide. E-A-T is fundamental to Google, in the guidelines they use the words expert, authority, and trust (or their variations) more than 200 times.
Let’s briefly look at each component of E-A-T:
- Experience: is the information accurate? Should this writer or company write about this topic?
- Authority: Is the author respected in his field? Is the company widely recognized in the industry? Is the content referenced elsewhere on the web by other websites, companies, and authorized persons?
- Trust – Do the company and writer have a good reputation and is the content trustworthy?
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Also published on Medium.