Machine Learning – 6 Ways How Search Engines Use It.

Machine Learning – 6 ways how search engines use it.

Machine learning has become a buzzword since 2017 and it continues to be so in this year as well. And why it wouldn’t be? It is the technology that making so many changes in our lives at such fast pace. We have also mentioned about this trend in our previous posts. We were reading that ML has made reforms in so many fields and we were obviously curious to know about how it works for search engines and SEO companies . Well, here we have tried to mention some of the learnings.

Oh, by the way, if you didn’t know already, Google has declared itself a machine learning first company! Isn’t it amazing?

Basically, Machine learning uses algorithms to calculate trends, values, and other mark-ups of specific things based on the data.

So, let’s see how Machine learning is used by the search engines.

There are many parameters which are considered while using the machine learning for the betterment of search engines and the search results that they present. Here are some of those –

1. Detecting the pattern

Pattern detection by machine learning helps the search engine identify the spam content or the duplicate content. This pattern detection has various attributes. When it comes to low-quality content that is been published, they have some common features. For example – Multiple stop words usage and heavy use of synonyms, many outbound links to irrelevant pages and other such variations. These kinds of patterns are easily detected through ML and it cuts down on the manpower that is required to review these parameters by actual people.

Google still has quality raters; we mean the actual people. But ML has helped them a big deal to skim through the webpages to eliminate the low-quality pages. You know what’s the interesting part? Just like us human beings, MI gets better with practice. That means, the more pages get analysed, the more accurate it becomes.

2. Specific queries and custom signals

When a research was conducted for this, the researchers used Yandex – a Russian search engine to assess the results for various queries. It was found that the types of results for different queries depended hugely on the phrasing or the query category.

This indicates that machine learning can prefer variables more or less heavily in the specific queries – more than other. In the whole, the study found that when the personalised searches were customised by machine learning, it increased the click-through rate of the results by around 10 percent.

As this research progressed and the user entered more search queries, it was observed that the CTR was constantly increasing. It is because of the learning that Yandex was acquiring about the user preferences and then presented the information using the previous experiences.

3. New signals identification

According to a source from Google, ML not only helps in identifying the patterns of the queries, but it also assists the search engine to identify the new ranking signals. These ranking signals are watched out for so that Google can continuously improve the search query quality results. It is said that as the time progresses, more and more of Google’s ranking signals may become ML based.

There will be lesser human intervention in the future as the search engines are able to show search engines how to use the predictions and data on their own. This means that the people can focus on the things that involve actual human inputs, for instance, things like innovations, human-centred approach, and so on.

Machine learning. Image from Shutterstock
Machine learning. Image from Shutterstock

4. Improved targeting and Ad Quality

Machine learning can be used to develop the ad ranking and it can influence the ad ranking. When the ML is applied, the bid amount, expected CTR, ad relevance, landing page experience, the context of the user’s search and other such parameters gets loaded in the system to determine the thresholds that are considered by Google for a particular keyword.

5. Identifying the similarities

This means that a query data is used by machine learning to identify and personalise the queries which a user makes after their initial query. But along with this, it helps in creating patterns in the data that moulds the search results for the other users as well.

If there’s a particular phrase or word that is uses as a slang might not have accurate search results, but as the usage increases by the time, machine learning is able to present more accurate results for the queries. It depends on the use of language and what do we mean by the words or phrases we use in the digital conversations.

6. Better understanding of the images

With more than 40 million photos uploaded each day to Facebook and Instagram, there’s an immediate need of analysing and cataloguing that huge volume of images, on a daily basis.

With machine learning, this task becomes easier because it can analyse the colours, shapes, and patterns and combines it with the existing data about the photos. It helps the search engine to understand what kind of image it actually is. Through this, it is possible to search something on google using a photo – Image search option.

However, no matter how much the people are worrying about the robots taking over the jobs, it’s not true. Sure, it’s transforming the lives and the work processes, it still doesn’t have a significant impact even on the SERPs yet. It is still the small part of the Google’s overall algorithm. The humans are irreplaceable, but when they apply their human touch to their processes.


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