Search engines use machine learning for pattern detection in order to improve efficiency. Although it’s pretty impossible to explain everything about how machine learning influences our lives in one short blog post, understanding the basics of machine learning and the influence which it has on the search engines can help you to gain some insight into improvements and updates regarding search algorithms, such as the famous Panda update.
To predict the outcome of future tests, scripts can use supervised machine learning with past outcomes, in order to determine a hypothetical line of prediction. This is presented in a series of graphs, which represent different factors such as the correlation between a number for duplicate content and a number for negative user reviews. Initially, it’s possible for anomalies to have a significant impact on the hypothesis, but as more reference material is added this effect is likely to diminish.
When analysing correlations between different variables, it’s also important to consider that it may not simply be the one variable which is causing a certain outcome. Using the example mentioned earlier of the correlation between duplicate content and bad user reviews, for example, is it simply the duplicate content which is causing the bad reviews? Or, can other variables be taken into consideration, such as bad website design, excessive use of banner ads on the site, the use of click bait, or a bad brand reputation? When using graphs, combining the forecast graphs of both the negative and positive often provides the optimum middle. The distinction which this provides is required for decision learning with a number of variables.
In order to create decision trees, search engines such as Google don’t only use their human quality raters. Numerous indicators such as bounce rate, links, visitor data and social signals and status can also be used as desired or undesired outcomes in order to create their own graphs for prediction. Mixing a range of different formulas of prediction and division along with multiple variables has the potential to create many data trees, with each addition to the data set being likely to change it slightly. Scripts can also use unsupervised machine learning without the need to teach it any outcome.
Certain website traits such as user behaviour, subtle textual cues, and differences in branding can all be difficult for humans to trace, but easy for machines to identify. These factors have all become much more important when it comes to optimising websites and advertisements, which is why machine learning is increasingly becoming more of a valuable asset to improving search engine algorithms. However, the news for website owners is that with machine learning having the ability to find and identify these small and subtle differences easily, there is more emphasis on good SEO than ever before.
With the help of machine learning, search engines can improve their algorithms thanks to hypothesis for prediction, correlation between different variables, and the discovery of small differences and traits which are significant.