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Machine Learning & Ranking Factors

Reading Time: 3 minutes One of Google’s massive innovations in it’s search engine over the years was leveraging machine…

Written by Joe Robison
Last updated 1 year ago

Reading Time: 3 minutes

One of Google’s massive innovations in it’s search engine over the years was leveraging machine learning as a core component of its search ranking factors.

No longer was Google purely creating its own ranking factors, it was leveraging ML and reinforcement learning, along with 10,000 quality raters, to teach the algorithm what it wanted certain factors to cater to for ideal outputs (there are many degrees of this, not a blanket statement).

How does Google use machine learning in its ranking factors?

Google uses machine learning in several ways to enhance its search engine functionality and the overall user experience. Here are some of the key ways in which Google uses machine learning:

  1. RankBrain: Google’s RankBrain is a machine learning-based component of Google’s core algorithm, which is used to understand the context and intent behind search queries. RankBrain is especially useful for processing and understanding the meaning of complex, multi-word queries or queries that have not been seen before. It can make educated guesses about what unfamiliar words or phrases might mean and how they’re relevant to each other, and then provide relevant search results based on these assumptions.
  2. Natural Language Understanding: Google uses machine learning to better understand and interpret natural language in search queries. This helps Google understand the context and semantics of words in a search query, enhancing its ability to deliver accurate search results. BERT (Bidirectional Encoder Representations from Transformers) is one such model used by Google, which helps it understand the context of words in searches and match those queries with helpful results.
  3. Personalization: Machine learning algorithms also help Google deliver personalized search results based on user’s past search history, location, device type, and other factors. This allows Google to provide more relevant and tailored search results to individual users.
  4. Spam Detection: Google uses machine learning to identify and filter out spammy or low-quality content in its search results, enhancing the overall quality and reliability of the search results it provides.
  5. Image and Video Search: Machine learning is also used in Google’s image and video search capabilities. Google can identify and categorize images and videos based on their content, making it possible to search for specific images or videos using text queries.
  6. Speech Recognition: Google uses machine learning in its voice search capabilities. The algorithms can understand and interpret spoken language, allowing users to perform searches using their voice.

Quick Summary of Machine Learning as Ranking Factors

Machine learning ranking factors are important for both Google SEO and as general concepts.

  • Google evaluates sites based on hundreds of ranking factors, and even new SEO trends have roots in the current algorithm[1].
  • Google’s machine learning technology called RankBrain is used to help sort through its search results[2].
  • Machine learning is where a computer teaches itself how to do something, rather than being taught by humans or following detailed programming[2].
  • The exact Google search algorithm is one of the most closely guarded secrets in the modern era[3].
  • A handful of factors have proven crucial in helping web pages rank higher, including speed, responsiveness, domain age, optimized content, and technical SEO ranking[3].
  • Google will continue adjusting ranking signals to best meet the needs of searchers, which means that even new SEO trends have roots in the current algorithm[1].
  • Machine learning can help with SEO by thinking in terms of topics rather than keywords, understanding customer intent, and using data to analyze challenges[4].
  • Google’s machine learning technology has led to a shift in user search behavior, more intuitive and complex search results, reprioritized Google ranking factors, and reduced language barriers[5].

Overall, while the exact ranking factors used by Google’s search algorithm are not known, there are some key factors that have been proven to be important for SEO. Additionally, machine learning technology is playing an increasingly important role in SEO by helping to sort through search results and analyze data.


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