# Ranking Algorithms & Types: Concepts & Examples - Data & Digital (2023)

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?

Confidence Intervals Formula, Examp...

Confidence Intervals Formula, Examples

(Video) Machine Learning Course - 23. ML Design Pattern - Ranking

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.

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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.

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## FAQs

### What are the algorithms for ranking data? ›

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.

What are the different types of ranking? ›

There are three main types of ranking: Standard competition ranking, ordinal ranking, and fractional ranking.

What is ranking and its concepts in machine learning? ›

Ranking is a machine learning technique to rank items. Ranking is useful for many applications in information retrieval such as e-commerce, social networks, recommendation systems, and so on. For example, a user searches for an article or an item to buy online.

What is the role of ranking algorithms for information retrieval? ›

Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. A majority of search engines use ranking algorithms to provide users with accurate and relevant results.

What are the 4 types of algorithm? ›

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What are the 5 best algorithms in data science? ›

Top Machine Learning Algorithms You Should Know
• Linear Regression.
• Logistic Regression.
• Linear Discriminant Analysis.
• Classification and Regression Trees.
• Naive Bayes.
• K-Nearest Neighbors (KNN)
• Learning Vector Quantization (LVQ)
• Support Vector Machines (SVM)
Aug 23, 2022

What is rank and examples? ›

The noun rank refers to a position within a hierarchy, and to rank something is to put it in order — for example, your high school might rank students in terms of their GPAs. You can also use rank to describe an especially foul smell, like the rank gym shoes in the back of your closet.

What is the example of ranking method? ›

In this method, one employee is compared to another employee. The end result is an ordering of employees from best to worst. For example, in a group of 'n' employees, performance of employee-1 is compared with performance of 'n-1' employees. Performance of employee-2 is compared with performance of 'n-1' employees.

What algorithm does Google use? ›

PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results.

What is a ranking model? ›

What is a ranking model? Ranking is a type of supervised machine learning (ML) that uses labeled datasets to train its data and models to classify future data to predict outcomes. Quite simply, the goal of a ranking model is to sort data in an optimal and relevant order.

### How do you create a ranking model? ›

1. Define Your Algorithm Goal. Defining a proper measurable goal is key to the success of any project. ...
2. Collect Some Data. ...
3. Define Your Model Features. ...
4. Train Your Ranking Algorithm. ...
5. Evaluate How Well You Did.
Mar 13, 2019

How do you implement a ranking system? ›

Elements of Successful Ranking Systems
1. Develop mission statement.
2. Define primary target audiences for the rankings.
3. Allow primary target audience concerns to help drive indicator formulation.
4. Cite sources for all input data.
5. Prioritize indicators of performance—even if data is not initially available.

What metrics would you use for a ranking algorithm? ›

There are various metrics proposed for evaluating ranking problems, such as:
• MRR.
• Precision@ K.
• DCG & NDCG.
• MAP.
• Kendall's tau.
• Spearman's rho.
Sep 22, 2020

What is the purpose of ranking method? ›

1. in industrial and organizational settings, a method of evaluating jobs for the purpose of setting wages or salaries; jobs are ranked according to their overall value to the company.

What is the purpose of ranking your data? ›

Ranking is one of the simple and efficient data collection techniques to understand individuals' perception and preferences for some items such as products, people, and species. Ranking data are frequently collected when individuals are asked to rank a set of items according to a certain preference criterion.

What are 3 examples of algorithms? ›

Common examples include: the recipe for baking a cake, the method we use to solve a long division problem, the process of doing laundry, and the functionality of a search engine are all examples of an algorithm.

What are the 5 categories of algorithms? ›

There are seven different types of programming algorithms:
• Sort algorithms.
• Search algorithms.
• Hashing.
• Dynamic Programming.
• Exponential by squaring.
• String matching and parsing.
• Primality testing algorithms.

What are the 10 algorithms every data scientist should know? ›

Alright, let's wrap this up! If you're a data scientist, you need to know these top 10 machine learning algorithms: K-Nearest Neighbors, decision trees, support vector machines, Naive Bayes, linear regression, logistic regression, artificial neural networks, random forest, gradient boosting, and clustering.

What is the most commonly used algorithm? ›

Decision Tree

Decision Tree algorithm in machine learning is one of the most popular algorithm in use today; this is a supervised learning algorithm that is used for classifying problems. It works well in classifying both categorical and continuous dependent variables.

What are the main types of algorithms? ›

The seven types of algorithms are the brute force-based algorithm, greedy algorithm, recursive algorithm, backtracking algorithm, divide and conquer algorithm, dynamic programming algorithm, and randomized algorithm.

### What is the formula for ranking? ›

=RANK(number,ref,[order])

The RANK function uses the following arguments: Number (required argument) – This is the value for which we need to find the rank. Ref (required argument) – Can be a list of, or an array of, or reference to, numbers.

What is rank in SQL? ›

The rank of a row is one plus the number of ranks that come before the row in question. ROW_NUMBER and RANK are similar. ROW_NUMBER numbers all rows sequentially (for example 1, 2, 3, 4, 5). RANK provides the same numeric value for ties (for example 1, 2, 2, 4, 5). Note.

How do you use rank in statistics? ›

By default, ranks are assigned by ordering the data values in ascending order (smallest to largest), then labeling the smallest value as rank 1. Alternatively, Largest value orders the data in descending order (largest to smallest), and assigns the largest value the rank of 1.

What is ranked data examples in statistics? ›

The ordinals 1st, 2nd, 3rd, and 4th can be associated with them, so your data is ranked. Other examples of ranked variables include time-ordered data such as the winners of a race or the order in which flowers emerge, and intensity-ordered data such as the stages of cancer.

What type of variables are ranking? ›

Ranked variables, also called ordinal variables, are those for which the individual observations can be put in order from smallest to largest, even though the exact values are unknown.

What statistical test for ranking data? ›

The Mann-Whitney U test is also known as the Wilcoxon Rank Sum test. It is appropriate when the data are actually ranks or when you do not want to assume the observations have a normal distribution within each group.

Which algorithm is used by Facebook? ›

EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed.

What are the 3 key ranking factors that Google uses in their algorithm? ›

What Are the Key Ranking Factors in the Google Search Algorithm?
• Meaning and Intent. Within Google's search algorithm, understanding and clarifying the meaning and intent of the search query is the key first step. ...
• Relevance. ...
• Quality. ...
• User Experience. ...
• Context.
Oct 7, 2021

What algorithm does GPS use? ›

Google Maps essentially uses two Graph algorithms – Dijkstra's algorithm and A* algorithm, to calculate the shortest distance from point A ( Source) to point B ( destination). A graph data structure is essentially a collection of nodes that are defined by edges and vertices.

What is graph based ranking algorithm? ›

Graph-based ranking algorithms are essentially a way of deciding the importance of a vertex within a graph, based on information drawn from the graph structure. In this section, we present three graph-based ranking algorithms – previously found to be successful on a range of ranking problems.

### What are the 4 metrics for evaluating classifier performance? ›

Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems.

What are the pros and cons of ranking method? ›

The advantages of the individual ranking method are it is easy to understand and use, it is easy to compare job performance, and it saves money and time. The disadvantages of the individual ranking method are it is not easy to practically compare each of the employees and for large organizations, it is not applicable.

Is XGBoost used for ranking? ›

XGBoost uses the LambdaMART ranking algorithm (for boosted trees), which uses the pairwise-ranking approach to minimize pairwise loss by sampling many pairs.

What type of data is ranking data? ›

Ordinal: the data can be categorized and ranked.

What is LambdaMART ranking algorithm? ›

LambdaMART is a technique where ranking is transformed into a pairwise classification or regression problem. The algorithms consider a pair of items at a single time, coming up with a viable ordering of those items before initiating the final order of the entire list.

What graph should be use for ranking? ›

Use bar charts to show data that are ranked, in either ascending or descending order. Horizontal bars should be used. A bar chart should always be ranked by value, unless there is a natural order to the data (for example, age or time).

What are the 7 types of algorithm based on concept? ›

The seven types of algorithms are the brute force-based algorithm, greedy algorithm, recursive algorithm, backtracking algorithm, divide and conquer algorithm, dynamic programming algorithm, and randomized algorithm.

What are the 7 algorithm design techniques? ›

9 Algorithm Design Techniques to Get Started With

Backtracking: Solving all possible combinations then backtracking if the current solution doesn't look desirable. Divide and conquer: Solving the problem by dividing it into sub-problems. Brute Force: Finding all the possible solutions and trying each one-by-one.

How do you create a ranking system? ›

Elements of Successful Ranking Systems
1. Develop mission statement.
2. Define primary target audiences for the rankings.
3. Allow primary target audience concerns to help drive indicator formulation.
4. Cite sources for all input data.
5. Prioritize indicators of performance—even if data is not initially available.

What is ranking scale method? ›

What is a Ranking Scale? A ranking scale forces respondents to rank a list of items with only one selection in each rank. Ranking scale questions often ask respondents to rank based on preference, but you can get creative with your ranking criteria.

### What are the 4 types of data collection? ›

Data may be grouped into four main types based on methods for collection: observational, experimental, simulation, and derived. The type of research data you collect may affect the way you manage that data.

What are the 3 types of data? ›

In this article, we explore the different types of data, including structured data, unstructured data and big data.

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