If you’re confused, it all comes down to this: Search is powered by hundreds of algorithms and machine learning models that generate signals that help determine how high-quality and useful pages are. The key to ranking is to consistently be the most useful result . To ensure that your site is the most useful result, you need to consider each of the Google’s useful content criteria They represent a generalized version Search Quality Assessment Guidelines.
The Role of Quality Assessors in Machine
Learning Algorithms On the Google ivory coast phone number library page “How does search work?” talks about the use of machine learning systems that help to better assess relevance. According to the company, this analysis uses “interaction data” – from people who interact with the search engine. They are called Quality Raters, or quality assessors. This data is then converted into signals. We mentioned several systems generating these above,
What do these interaction data represent?
INGoogle’s documentation on how Quality Raters are used, two sources are mentioned. Interaction Data: The Role of Real-Time Search Real-time experiments help machine learning models determine whether they are doing a better job of recognizing useful content. If not, the systems can learn which features to weight more heavily in their algorithms to achieve even better results. Google wants its models to be really good at recognizing quality content.
To do that, they need to show them lots of examples of useful and unhelpful results.
The more examples there are, the better the distinctions between these variants the model is at predicting the right combination of factors to consider to determine quality. How does Google get so many examples? The company has a whole team of people who provide them. Who are these assistants? This is16,000 quality assessors. Quality Raters: Identifying Useful and Unhelpful Pages Google has a document that every SEO should read several times calledQuality Raters’ Guidelines(QRG, or Quality Raters Guide) – This is essentially a textbook that helps raters understand what makes content useful to humans.
These guidelines describe the materials used to build AI models.
The content usefulness criteria represent buying house b a generalized version of the recommendations proposed in the QRG. The “helpful” and “not helpful” labels are invaluable to Google’s machine learning algorithms. By classifying search results this way, quality raters provide real-world feedback that the algorithms can learn from. How is the usefulness of a page determin? Quality Raters use a rating scale that takes into account how well the content meets the user’s needs.
