Ranking algorithms are used to rank items in a dataset according to some criterion. Ranking algorithms can be divided into two categories: deterministic and probabilistic. Ranking algorithms are used in search engines to rank web pages according to their relevance to a user’s search query. In this article, we will discuss the different types of ranking algorithms and give examples of each type.
What is a Ranking Algorithm?
Ranking Algorithm is a procedure that ranks items in a set of data on web pages or on any app. Ranking Algorithm ranks any item at any place only by using some of the criteria, the criterion by which any of the data is ranked is decided by the creator of that particular set of data.
Ranking Algorithms mainly use 3 techniques to rank any of the data in a data set. The techniques are mentioned below:-
Crawling is the process by which search engines discover updated content on the web, such as new sites or pages, changes to existing sites, and dead links.
To do this, a search engine uses a program that can be referred to as a ‘crawler’, ‘bot’ or ‘spider’ (each search engine has its own type) which follows an algorithmic process to determine which sites to crawl and how often.
As a search engine’s crawler moves through your site it will also detect and record any links it finds on these pages and add them to a list that will be crawled later. This is how new content is discovered.
Once a search engine processes each of the pages it crawls, it compiles a massive index of all the words it sees and their location on each page. It is essentially a database of billions of web pages.
This extracted content is then stored, with the information then organized and interpreted by the search engine’s algorithm to measure its importance compared to similar pages.
Servers based all around the world allow users to access these pages almost instantaneously. Storing and sorting this information requires significant space and both Microsoft and Google have over a million servers each.
As SEO this is the area we are most concerned with and the part that allows us to show clients tangible progress.
Once a keyword is entered into a search box, search engines will check for pages within their index that are the closest match; a score will be assigned to these pages based on an algorithm consisting of hundreds of different ranking signals.
These pages (or images & videos) will then be displayed to the user in order of score.
So in order for your site to rank well in search results pages, it’s important to make sure search engines can crawl and index your site correctly – otherwise, they will be unable to appropriately rank your website’s content in search results.
To help give you even more of a basic introduction to this process, here is a useful video from Google which explains it quite well. Each search engine follows a similar methodology to this.
Types of Ranking Algorithm
There are many types of Algorithms in Search Engines with their own advantages. Some of them are mentioned below:-
- Ranking by similarity
- Ranking by Probability
- Binary Ranking Algorithms
Ranking by similarity
Ranking by similarity is a type of probabilistic ranking algorithm that ranks items in a dataset according to their similarity to a reference item. The reference item is the item that is used to calculate the similarity value for each of the other items in the dataset. The ranking algorithm uses the input data, such as the number of features that are common to both positive and negative examples, to calculate the item’s relevance score. The higher the relevance score, the more similar the item is to the reference item. There are many different types of ranking by similarity algorithms, each with its own set of advantages and disadvantages. Some common types of ranking by similarity algorithms are clustering ranking algorithms, vector space ranking algorithms, etc.
Ranking by probability
Ranking by probability is a type of probabilistic ranking algorithm that ranks items in a dataset according to their probability of being a positive example. The ranking algorithm uses the input data, such as the number of features that are common to both positive and negative examples, to calculate the item’s relevance score. The higher the relevance score, the more likely the item is to be a positive example. Ranking by probability is different from other types of ranking algorithms because it takes into account the uncertainty of the data. This makes it more accurate than other types of ranking algorithms. There are many different types of ranking by probability algorithms, each with its own set of advantages and disadvantages. Some common types of ranking by probability algorithms are Bayesian Ranking Algorithm, AUC Ranking Algorithm, etc.
Binary Ranking Algorithm
Binary ranking algorithms are the simplest type of ranking algorithm. A binary ranking algorithm ranks items in a dataset according to their relative importance. The two most common types of binary ranking algorithms are rank-by-feature and rank-by-frequency algorithms. Rank-by-feature algorithms rank items by the number of features that they have in common with the reference item. The reference item is the item that is used to calculate the similarity value for each of the other items in the dataset.
Page rank is important because it’s one of the factors a search engine like Google takes into account when it decides which results to show at the top of its search engine listings – where they can be easily seen. (In fact, PageRank is a Google trademark – but other search engines use similar techniques.)
It’s not the only or even the most important factor. To start with, your page needs to be relevant to whatever the search is – if someone’s searching for plumbers, your page about online banking isn’t going to feature no matter how high its page rank may be. But all other things being equal, page rank does have an effect on search engine rankings.
The conclusion is that the webpage you think is important and if you want to rank the page you want you should follow certain steps to rank your page. Ranking Algorithm some of its techniques to do so.
Anyone who has any doubt or still not understood how the ranking algorithm can refer to the video linked below,
You can refer to this video for educational purposes.
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