The latest buzzword around Google and the overall search and technology market is the increasing role of artificial intelligence and machine learning to offer a more enhanced user experience. Google, the search engine giant, makes use of machine learning in conjunction with RankBrain (which re-ranks based on actual signals) to offer users more relevant search results. This search engine provides you with a hassle-free search experience by delivering you the exact information you are looking for when searching online.
A high degree of machine learning involvement ensures that the search engines use more sophisticated algorithms to find content on the internet. Careful analysis of past patterns delivers better search engine results over time. Before the advancements of machine learning and artificial intelligence, blackhat techniques were used to trick search engines into providing less than optimal results. These “black hatters” were using unscrupulous methods to manipulate their rankings on the web.
Machine learning also proves to be a highly useful tool in the fight against the scourge of spam. According to industry reports, the number of spam messages in 2018 ran not into the billions but trillions. There is little a person can do other than grinding his/her teeth in frustration after seeing their inbox stuffed with unsolicited messages and ads. Low barriers to entry and the almost negligible cost have made spamming a favorite activity of individual organizations and webmasters.
Users have been subjected to a fantastic variety of fraud on a daily basis thanks to this flow of spam. Internet service providers also suffer a lot as they have to add extra bandwidth to cope with the amount of spam that delivers to inboxes each day.
However, before we delve further and learn how the use of machine learning improves search engine quality, it is essential to understand how search engine crawlers or robots work and how they make use of algorithms to rank web pages.
As mentioned above machine learning is generating much buzz in the search engine space, as well as, the overall technology market. It is therefore vital to understand how machine learning is being employed by search engines to impact search and SEO.
It would be quite complicated to analyze or understand how search engines use machine learning without knowledge about what machine learning is and why it is generating such enthusiasm all over the world. So, we will first look at what machine learning is before getting into why Google’s machine learning is the future of SEO.
Machine learning is all about telling a computer how to act based on historical data and patterns without being explicitly programmed. Machine learning is often confused with artificial intelligence (AI) and people think of them to be the same. However, please note that AI is a vast domain and machine learning is just a part (although an essential part) of AI. As noted above, machine learning is all about enabling computers to conclude without any explicit programming instructions. AI, on the other side, is the science behind creating systems, which processes information to a considerable extent in a way similar to that of a human brain.
How does Machine Learning Language Work?
A standard machine learning model works in the following ways:
- Provide the system with a known set of data containing a large number of possible variables either with a positive or negative outcome. The reason for this is to train the system and give the system something to start working on initially.
- Make provisions for rewards when the system can identify unknown entities based on its acquaintance with the starting data.
- Let the system go once the results are satisfactory or the system has successfully crossed the initially set threshold.
Machine Learning and Search Engines
Search engine companies, as well as scientists, are trying to evolve machine learning and then harness its power to give users rewarding search experiences. Google is taking the most active initiative in this direction and is even offering a free course on it.
Things do not stop here as Google has gone a step further and has made its machine learning framework, TensorFlow, open source. The search engine giant is also making a considerable investment in developing state of the art hardware that could support machine learning. Keeping this in mind, we will now look at some of the more critical applications of machine learning Google is employing in the following sections.
No article on machine learning, especially if it is related to Google, would be complete without mention of their first and most crucial implementation of a machine learning algorithm to deliver relevant, engaging, and high-quality results. We are of course referring to RankBrain.
The RankBrain system was primarily designed to understand entities which could be a distinct and unique entity. The system then tried to determine an understanding of how these entities connect to a query to offer a more wholesome search experience. In other words, Google provided the system queries and a likely set of entities. The system would then be programmed, or to put it more aptly, trained, to identify new entities based on its study of previous entities.
Once the system develops the ability to perform the entity matching tasks satisfactorily, the next step would be to train it to teach itself to comprehend more accurately the relationships between entities and what data is being requested and then deliver results accordingly.
In a nutshell what RankBrain does is:
- Keeps on learning about the relationship between different entities.
- Comprehends the words that are synonyms and those that are not and display results accordingly.
- Deals with other portions of the algorithm to deliver better results on the results returned by it.
These activities allowed RankBrain to deliver superior results for queries that display non-optimized results and involve an amalgamation of new and old entities to offer more rewarding search results to users.
Machine learning allows Google to deliver results by better understanding the nature and reference of queries. For example, a question for fixing my car may return the result for mechanics whereas the need to replace my vehicle may refer to a whole gamut of activities ranging from part replacements to the need for official documentation to replace the entire thing.
Spam and Machine Learning
Spamdexing and the Harm it can Cause
The word ‘Spamdexing’ is a combination of two words ‘spam’ and ‘indexing.’ Therefore as the name suggests, it refers to conscious manipulation of search engine indexes.
Spamdexing, also known as search engine poisoning, is the deliberate attempt made by unethical webmasters to tamper with the search engine indexes.
Search Engine Indexes — What are They?
Search engines have automated programs called crawlers, spiders, or robots that visit different sites on the Internet. This software checks for the website’s meta tags, the quality of information it contains, the traffic it attracts, and the number of links originating to and from it, among other things.
All this information plays a part in the building of indexes. After the spiders collect the data, the spider or crawler returns to the central depository where the data is indexed.
The spiders or crawlers visit these websites at certain intervals (determined by search engine administrators) to see if there are any changes made to the website.
People like you and me, when looking for any information on the internet through a search engine, are getting the result from the indexes the crawlers have created. The reason search engines can deliver results so quickly is that the results are indexed. The search engine does not have to look for information on the internet but only in the indexes created by the engines. There are ways in which website owners can get website pages indexed more quickly, which is a good thing.
Indexes are going to be the prime target of webmasters and spammers looking to manipulate the indexes to get a higher ranking for their websites. Various dishonorable and questionable methods and techniques used by unprincipled and unprofessional webmasters to secure a higher ranking in the results displayed by search engines is known as Black Hat Search Engine Optimization (SEO) techniques.
Machine Learning to Help Solve the Problem
A majority of people these days use Gmail as their primary email delivery service. Google, to ensure that users avoid spam, makes much use of machine learning. Google claims that it is now in a position to effectively block closer to 99.9 percent of spam and phishing emails with a commendable false-positive rate of only 0.05 percent.
Extensive use of machine learning is at the core of this spam blockage. The concept is pretty simple to start with. Feed the machine with spam messages and let machine learning build a model around it to identify spam messages.
Once this is complete, enter in new messages and see if the machine can identify the spam based on its previous interaction with similar words. As time passes, the device will learn far more signals and, as such, perform the desired actions faster than ever possible for human beings.
The same approach listed above identifies spammy or blackhat type SEO efforts. Google identifies these approaches via machine learning and penalizes the connected websites. Today, the results we receive from Google and its services like Gmail are much, much better.
By doing the above, Google is able to create a much better and more precise search engine. No longer will black hat methods work to trick search engines and rankings will be determined based off of quality digital marketing strategies and efforts. Machine learning is the future and how SEO professionals adapt to the rules of the machine will determine how successful they are in the SEO space.