11 months ago on
It is very important that you understand the manner in which search algorithms work, how search engines crawl and index various websites and the ways in which search engines treat the intentions of users.
Before you go into understanding the impact between these two concepts, you need to know what Machine Learning is all about.
Machine Learning is a technology that is employed in every segment of any system linked to major search engines like Bing or Google.
After having learnt about Machine Learning (ML), you will now be informed about the various applications of this technology especially with regard to search engines.
1. Helps To Understand Queries
It is an interesting piece of trivia to learn that ML can be applied to everything that demands the recognition of search queries. Through ML, it becomes a breeze to understand the search query that is keyed in by the user on the address bar. Here are a couple of instances that explain the significance of ML to search queries.
You should know that search engines operate through a number of classifiers that work on a search query. ML can help differentiate between a navigational query as against an informational query or a transactional query, news query in contrast to a local intent query or a shopping query.
It is a common phenomenon for you to notice a list of suggestions that show up on your screen after you start keying in the first few alphabets of your search string. This is the gift from ML to search.
ML helps search engines to use synonyms that can expand the query keywords, ultimately increasing the set of search results.
It is very important for search engines to furnish search results after understanding the primary intent of the user through Word-Sense Disambiguation techniques. For example: if ‘ice cream’ is the search word, should the search engine provide results relating to “ice cream parlours near me”, or “ice cream cake” or ice cream recipe”. Or should it furnish results for all the 3 options? That means, ML helps Google or Bing to satisfy a user’s intent by showing up options that are in sync with the most-frequently used search terms in the past.
2. ML Facilitates Search Ranking
You should know that search engines operate through multiple ranking phases that take place in a sequence. These are: initial retrieval, primary ranking, and contextual ranking along with personalized ranking. ML through the Learning to Rank application can be effectively used to provide ranks to all these different phases.
3. To Better Understand The Document or The URL
Anything that can be done to understand a search result can be possible through ML. That means, you can recognize the various types of pages whether it is a news site, a forum or a blog. You can detect spam and junk along with the identification of inferior-quality URLs. To explain this, as a user of any email system you are deriving the benefits of ML when it effectively blocks 99.99% of all the spam mails.
ML also steps in to perform Sentiment Analysis. This is an automatic process to identify whether a user-text is positively, negatively or neutrally inclined to a particular event, topic, person or product.
ML also helps you to detect various entities including places or people that show up on a web page and the relationships between them.
4. Allows to Come Up With Additional Content
ML helps to analyze the link structure of your site so that it can show up shortcuts which are called as Sitelinks. Through these links, users will be able to navigate your site without wasting time on unnecessary pages and immediately land on a page that they are looking for.
5. Crawlers Can Make the Most of ML
Through ML, crawlers can identify the importance of a particular URL depending on the number of updates it went through and then come up with an optimal rate to crawl that URL.
All the above features are available to search through the “intelligent” technology called Machine Learning which can be imagined as a Google Engineer. It is through this unseen Google Engineer that every web surfer can adjust what he/she sees before the search is made on a particular device.
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