# Ranking Algorithms & Types: Concepts & Examples

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 webpages 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?

A ranking algorithm is a procedure that ranks items in a dataset according to some criterion. Ranking algorithms are used in many different applications, such as web search, recommender systems, and machine learning.

A ranking algorithm is a procedure used to rank items in a dataset according to some criterion. Ranking algorithms can be divided into two categories: deterministic and probabilistic.

• Deterministic ranking algorithms: A deterministic ranking algorithm is one in which the order of the items in the ranked list is fixed and does not change, regardless of the input data. An example of a deterministic ranking algorithm is the rank-by-feature algorithm. In this algorithm, each item is assigned a rank based on its feature value. The item with the highest feature value is assigned a rank of 1, and the item with the lowest feature value is assigned a rank of N, where N is the number of items in the dataset. One real-world application of a deterministic ranking algorithm is the ordering of items in a grocery store. The items in a grocery store are usually organized by department, such as produce, meat, dairy, etc. Within each department, the items are usually organized alphabetically. This type of organization is an example of a deterministic ranking algorithm. Sorting algorithms are used in deterministic ranking algorithms to order the items in the ranked list. There are many different types of sorting algorithms, each with its own set of advantages and disadvantages. Some of the most common sorting algorithms are insertion sort, merge sort, and quicksort.
• Probabilistic ranking algorithms: In a probabilistic ranking algorithm, the order of the items in the ranked list may vary, depending on the input data. An example of a probabilistic ranking algorithm is the rank-by-confidence algorithm. In this algorithm, each item is assigned a rank based on its confidence value. The item with the highest confidence value is assigned a rank of 1, and the item with the lowest confidence value is assigned a rank of N, where N is the number of items in the dataset. Another example of a probabilistic ranking algorithm is the Bayesian spam filter. In this algorithm, each email is assigned a probability of being spam. The emails with the highest probabilities are ranked first, and the emails with the lowest probabilities are ranked last. Probabilistic ranking algorithms can be used in web search engines to rank webpages according to their relevance to a user’s search query. The ranking algorithm uses the input data, such as the number of links to the webpage from other websites and the number of times the keyword appears on the page, to calculate the page’s relevance score. The higher the relevance score, the higher the page is ranked in the search results. The probabilistic ranking algorithms can as well be used in machine learning algorithms to rank items in a dataset according to their likelihood 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 it is that the item is a positive example. There are many different types of probabilistic ranking algorithms, each with its own set of advantages and disadvantages. Some common types of probabilistic ranking algorithms are:
• Bayesian Ranking Algorithm: A Bayesian ranking algorithm is a probabilistic ranking algorithm that uses a Bayesian network to calculate the item’s relevance score. The Bayesian network is a graphical model that represents a set of random variables and their conditional dependencies. The Bayesian 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 it is that the item is a positive example.
• Log-linear Model Ranking Algorithm: A log-linear model ranking algorithm is a probabilistic ranking algorithm that uses a log-linear model to calculate the item’s relevance score. The log-linear model is a mathematical model that describes the relationship between two or more variables in terms of a linear combination of the logarithms of the variables.

One of the most common applications of ranking algorithms is in search engines. Search engines use ranking algorithms to determine which webpages are most relevant to a user’s search query. Ranking algorithms are also used in recommendation systems to recommend items that a user may be interested in. The following is a quick overview on ranking algorithm used by popular search engines:

• Google Ranking Algorithm: Google’s ranking algorithm is a secret, but we know that it is a probabilistic ranking algorithm. Google uses a variety of factors to rank webpages, including the number of links to a page, the page’s PageRank, and the relevance of the search query to the page. Google’s PageRank algorithm is a probabilistic ranking algorithm that uses the number of links to a webpage as a measure of its importance. The higher the PageRank of a webpage, the more likely it is to be ranked higher in the search results.
• Amazon Ranking Algorithm: Amazon’s ranking algorithm is also a probabilistic ranking algorithm. Amazon uses a variety of factors to rank items, including the number of reviews an item has, the average rating of an item, and the price of an item. Amazon’s algorithm is designed to recommend items that are relevant to a user’s search query and that are popular with other users.
• Facebook Ranking Algorithm: Facebook’s ranking algorithm is a secret, but we know that it is a probabilistic ranking algorithm. Facebook uses a variety of factors to rank news stories, including the number of likes, shares, and comments a story has, the story’s PageRank, and the relevance of the story to the user’s News Feed. Facebook’s algorithm is designed to show users the stories that are most relevant to them and that are being talked about by their friends.
• Twitter Ranking Algorithm: Twitter’s ranking algorithm is also a probabilistic ranking algorithm. Twitter uses a variety of factors to rank tweets, including the number of retweets, favorites, and replies a tweet has, the tweeter’s PageRank, and the relevance of the tweet to the user’s timeline. Twitter’s algorithm is designed to show users the tweets that are most relevant to them and that are being talked about by their friends.

## Types of Ranking Algorithms

There are many different types of ranking algorithms, each with its own set of advantages and disadvantages. Some of the most common types of ranking algorithms are:

• Binary Ranking Algorithms: 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 the rank-by-feature and the 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. Rank-by-frequency algorithms rank items by the number of times that they occur in the dataset. Both rank-by-feature and rank-by-frequency algorithms have their own set of advantages and disadvantages. Rank-by-feature algorithms are more accurate than rank-by-frequency algorithms, but they are also more computationally expensive. Rank-by-frequency algorithms are faster than rank-by-feature algorithms, but they are less accurate.
• 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 algorithm, vector space ranking algorithm, etc.
• Ranking by Distance: Ranking by distance algorithms are a type of probabilistic ranking algorithm that rank items in a dataset according to their distance from a reference item. The reference item is the item that is used to calculate the distance 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 distant the item is from the reference item. There are many different types of ranking by distance algorithms, each with its own set of advantages and disadvantages. Some common types of ranking by distance algorithms are Euclidean distance algorithm, Mahalanobis distance algorithm, etc.
• Ranking by Preference: Preferential ranking algorithms are a type of probabilistic ranking algorithm that rank items in a dataset according to their preference for a reference item. The reference item is the item that is used to calculate the preference 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 preferred the item is for the reference item.
• 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.

## Conclusion

Ranking algorithms are used to rank items in a dataset according to some criterion. There are many different types of ranking algorithms, each with its own set of advantages and disadvantages. Ranking by similarity, distance, preference, and probability are the most common types of ranking algorithms. Ranking by probability is the most accurate type of ranking algorithm because it takes into account the uncertainty of the data. If you would like to learn more about ranking algorithms, please drop a comment below.

## Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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