RankBrain – The Latest Ammo in the Google Algorithm
It has been many years now that the capabilities of search engines have improved tremendously. Search queries have become much more complex and now comprise of elaborate questions that the Google algorithm has to decipher and then fish out the most relevant pages and present it in the order of merit in the search results. The Google algorithm is a system that is used to process searches and refine the results. It is well known by the name – Hummingbird.
There are many components of the algorithm, which contribute to its overall functioning. Top Heavy is a component of the Google algorithm that takes care of identifying pages loaded with advertisements so as to place it at the lower level of ranking. Similarly, Pigeon is deployed for improvement of local results. Copyright infringement is detected by Pirates and Payday, Penguin and Panda are dedicated for fighting spam. RankBrain is the latest addition to the Hummingbird armoury. These components work in unison to deliver the best results and enhance the functioning of the algorithm.
Beyond exact terms
Of the billions of searches that Google has to handle each day, there are millions of searches that do not constitute of the exact words that are being looked for. Instead, it contains some other words that are used to describe the search query. Google had its own ways of handling these types of queries so that the results include more variations that are closely relevant to what is being searched for. For example, when searching for “watch” Google would also include “watches” in the search results by using a feature in the algorithm known as “stemming.” It allows identification of the word “watches” as a variant of the word “watch”. Similarly, the synonym smarts feature is used to identify similar things that are known by different names like “sneakers” are included in the range of “sports shoes”. Distinguishing between the features of same names is achieved by using the “conceptual smart” feature that tells about apple being a fruit and Apple is technology.
The word connection
Searches that used to focus on strings of letters shifted its vision to things instead. This was made possible by the introduction of The Knowledge Graph that enriched the Google algorithm in 2012. The emphasis that used to be on letters now moved over to the word itself. Everything that is related to the word is considered for responding to the search query. The database of facts stored in The Knowledge Graph help to establish the relationships of things related to the word.
RankBrain is different
RankBrain is the latest feature that has been added to the algorithm to refine search queries. But why is RankBrain being specially highlighted when Google did not spend too many words for its predecessors? The reason is that RankBrain has been constituted much differently that the others. All the queries that are refined by synonym lists, stemming lists and interconnecting databases are linked to some human interface in the process. It is a combination of human work and automation. But RankBrain uses machine reading artificial intelligence to process its search results that completely does away with the human dependence.
Completely automated process
Not only is the working process of RankBrain completely automated but the technology of machine learning empowers it with the ability of self learning – it can learn and teach itself how to do things instead of depending on human inputs like programming. This has been made possible by using artificial intelligence which replicates the functioning of human brain.
Too much for humans
The enormous volume of searches, 3 billion a day, that Google has to handle and the growing complexities of search queries have been quite challenging for Hummingbird. It needed more powerful systems to process complex search queries. Till 2013, almost 15 percent search queries that translate to 450 million searches are completely new that the algorithm handles for the first time. The numbers can be quite numbing for humans to handle, hence the need of a system like RankBrain that is completely driven by technology which assures faster processing with better capabilities.
Handling complex queries
Interpreting multi-word queries that are quite complex is a specialty of RankBrain that is equipped with capabilities of interpreting queries in the best way, and translating them effectively so that the most relevant and best pages are presented in the search results. It can detect remote similarities between searches that are apparently not connected and use it for its own learning. This knowledge will be useful later to interconnect topics with complex searches. The most important thing is that it can associate the grouped searches with results that are most relevant. It has the ability to make the ambiguous more understandable by shedding the cover of ambiguity to convert it into something more specific that leads to better answers. The scope of the search is thus much widened and the searcher gets more poignant results.
As told earlier, RankBrain is a self-learner and is always learning. The learning process is offline. It learns to make predictions from historical batches of searches that are in store. The predictions are tested and if found viable are included in the updated version of RankBrain. It is a never ending learning process that enriches its knowledge and enhances its capabilities.
A ranking factor
Among the signals that are most important, content and links occupy the top two spots. By Google’s own admission, RankBrain is the third most important signal that it uses in the Hummingbird algorithm. The contributions of RankBrain are directly influencing page rankings. It has the ability to summarise page content in a much better way that enables better classification of pages that contribute to improved page ranking. The modalities of ranking are being surmised as Google has only hinted at it.
RankBrain has already performed better than human brains in identifying search pages more effectively. We will have to wait and see how much it can improve upon its success rate of 80 percent as compared to 70 percent scored by humans.