Search Engines & Machine Learning

Machine learning is essentially the use of algorithms to determine trends, values, and other characteristics based on historical data. Since Google has declared itself a company that puts machine learning first, it’s important to understand the impact that machine learning is having on SEO and how this is likely to evolve in the future. 

Pattern Detection

Search engines will use machine learning to detect patterns and help identify duplicate content and spam. For example, they detect patterns such as high use of stop words or synonyms, the presence of multiple outbound links to pages that are not relevant or related, and other low-quality content attributes. 

The use of machine learning to detect these patterns has enabled search engines to significantly reduce the manpower that it takes for humans to review everything. According to ESBO Ltd, this increased use of machine learning has only heightened the need for companies to focus on producing high-quality SEO content. 

Image Search

Millions of photos are uploaded each day to various sites and social networks such as Facebook, Instagram, and Flickr. This has led to the need for high volumes of photos to be cataloged and analyzed on a daily basis online.  

It’s a task that is ideal for machine learning, since it can analyze the patterns of colors and shapes, pairing this with any current schema data about the photo to help search engines understand what’s actually in an image. As a result, Google is able to catalog images not only for Google image search results, but also allow users to search using an image file rather than a text query to find other instances of the photo and similar images online. 

Custom Signals

According to a study conducted at the University of Washington in July 2017, machine learning in search engines can vary depending on the phrasing or category of the query. Researchers looked and analyzed results for different queries using Russian search engine Yandex, finding that the results depended significantly on the category or phrasing of the query. 

As a result, it’s clear that machine learning can place more or less significance on variables in certain queries. 

Overall, personalized searches that were customized by machine learning increased click-through rates by around 10%, which increased as the user entered more queries into the search engine. This is likely to be due to the fact that the search engine was able to learn about the preferences of that user and base information on past queries to ensure that the results were as relevant and interesting as possible. 

Identifying Word Similarities in Search

Query data is not only used by machine learning to identify and personalized user queries but it can also be used to create data patterns that shape the search results other users get. 

For example, Google Trends utilizes machine learning to display more accurate information for words or phrases that might not always mean something initially. 

As language develops and transforms with online trends, machine learning allows search engines to better predict the meanings behind the words that are searched for and provide users with more relevant and interesting information. 

Although machine learning is not and is unlikely to ever be perfect, it is becoming more accurate and smarter as we interact with it more often, which is having an increasing impact on SEO. 

Boris Dzhingarov is a digital nomad. He is the CEO of ESBO ltd .