Using Machine Learning In Google Search
Google has been evolving and redesigning itself in the way it connects users to the right content they are searching for. They have made huge progress towards this goal, with Google AI and the BERT algorithm. But the search giant has not been vocal about how machine learning is changing the weights attached to the various ranking signals. Here is our take on how search engines are using machine learning right now.
- Pattern Detection
Search engines are using machine learning for detecting patterns that help them detect spam or duplicate content. Low-quality content usually has clear similarities, like:
The presence of many outbound links to disconnected and unrelated pages.
Usage of stop words or synonyms.
Frequency of identified “spammy” keywords.
Machine learning easily identifies these patterns and flags them. It also uses data from user interactions to identify new spam structures and techniques being used, deciphers new patterns, and successfully flag those, also.
Although Google still uses human quality raters, using machine learning to identify these patterns significantly brings down on the man-hours necessary to review the content.
Also Read: Google’s Link Spam Related Algorithm Update
Likewise, Google is also able to scour through pages to filter out low-quality content without any human intervention.
Machine learning is a constantly evolving technology, so the more pages that are analyzed, the more precise it is.
- Identification of New Signals
Google’s machine learning algorithm, RankBrain not only helps identify patterns in queries but also helps the search engine discover new ranking signals.
Early on, algorithms used by Google were coded entirely by hand. It relied on a group of engineers to scrutinize search query results, run tests to enhance the quality of those results, and implement those changes.
Now, while there are still human brains behind the algorithm Rank Brain silently works in the background running tests and weighing in how the changes affect user interactions.
Rank Brain solves some of the tricky issues that Google faced with traditional algorithms- involving how to handle search terms that have never before been into Google.
As search engines are evolving to teach techniques on how to run predictions and data by themselves, there can be less human effort and employees can diver towards other things that machines can’t do, such as innovation or human-centered projects.
- It’s Considered as a Small Portion
Although machine learning is slowly evolving the way search engines find and rank websites, it doesn’t imply that it has a significant impact on search engine results.
During a 2019 Webmaster Central Office Hours discussion, Google’s John Mueller referred to how machine learning helps Google’s engineers understand more about various issues, but he also noted that “machine learning isn’t just this one black box that does everything for you where you feed the internet in on one side and the other side comes out search results.”
Also Read: A ‘How to Guide’ to App Store Optimization
Fairly recently, in May 2021, he elaborated that machine learning may adjust the significance of other ranking signals. That being said, there are still human brains manually checking and adjusting those values.
Google’s ultimate objective is to use technology to provide a better user experience. Also, there is no point in automating the entire process at the expense of the user experience.
So this means that machine learning will not take over search ranking any time soon, but implies that it is a small piece of the puzzle search engine have applied to make our lives better.
- Natural Language Processing
There is a significant amount of importance attached to how search engine recognizes how similar one piece of text is to another. This applies not just to words alone but also to the meaning and context attached to them.
Bidirectional Encoder Representations from Transformers (BERT) is a natural learning processing framework used by Google to have a better understanding of the context of a user’s search query.
This is because language is dynamic and constantly evolving, and new turns of phrases come up when we play with language.
The same word is used in different scenarios to describe different things. Having said that, as more people are using and searching new phrases online, machine learning is used to display more precise results for those queries.
Google Trends is a good example of this. As a new phrase or word gains traction, the early results may give non- sensical search results at first.
BERT is designed to mirror human recognition as precisely as possible to decipher those contextual nuances by learning more about user interaction with the content and corresponding search queries with more relevant results.
As language evolves machines are better able to understand the context behind those words we use and provide us with better information.
- Image Search to Understand Photos
Each second on average more than 1087 images are being uploaded on Instagram, while 4000 images are being uploaded on Facebook. That’s 100 million photos being uploaded on two major social media platforms alone. Analyzing and categorizing that many images are not humanly possible, but an easy task with machine learning.
Machine learning analyzes color and shape patterns and links them with any existing schema data about the image to help the search engine comprehend what an image in effect is.
This way Google can catalog images for search results but also enables a reverse image search, which allows users to search using an image in place of a text query.
Users can also find other related instances of the photo online, along with other photographs with the same subjects or color palette and information about the subjects in the image.
Consecutively, the user interactions with these results can also influence their SERPs in the future.
- Ad Quality & Targeting Improvements
Much like its organic search results, Google wants to provide targeted ads for individual users. This implies that Ad Rank can be influenced by machine learning.
Bid amount expected click-through rate, ad relevance landing page experience, and other factors like Ad Rank thresholds, the context of the person’s search is factored into the system, on a keyword-by-keyword basis, to ascertain the thresholds considered by Google for each keyword.
- Synonym Identification
When search results reflect words that are not included in the search phrase, it is possibly due to the influence of Rank Brain that identifies synonyms. For instance, while searching for environmental conservation, various results might also reflect the words “preservation” which is also used in the same context.
- Query Clarification
User intent might vary for each search like buying (transactional), research (informational), or find resources (navigational).Moreover, a single keyword could be useful to one or any of these intents.
By evaluating click patterns and the content type that users engage with a search engine can make use of machine learning to determine the intent behind the user’s search.
Machine learning is less than perfect and it is in its growing phase. The more humans interact with it so does the precision and accuracy of the results. Thus usage of technology with better user experience to solve complex problems will allow humans to focus more on powering creativity and innovation. If you need more help in using technology to improve your SERP results, contact us.