Algorithms: what they are, how they work, and where we use them

Understanding algorithms is crucial for anyone aspiring to master the dynamics of SEO and digital marketing more generally. Indeed, algorithms underlie many automated processes and play a crucial role in filtering and sorting information on the Web. Search engine algorithms, in particular, may appear to be “mysterious” machines, but technically they are a complex of mathematical formulas, rules and criteria that, when put into a system, determine what most people, starting with ourselves, see when they browse the Web, because they are in charge of analyzing all the web pages in the Index to then proceed to their ranking, and thus to the order in which the various pages appear in the SERPs, the search engine results pages themselves. In a sense, then, algorithms are the backbone of the way search engines such as Google, Bing, and Yahoo! perform the ranking of web pages to be shown in response to a user’s search query, and while it is impossible to fully unravel the “mystery,” today let’s try to understand how these systems work and what impact these technologies have on SEO and online visibility optimization.

What are algorithms?

Algorithms are finite sequences of defined instructions or rules that describe a process or calculation to solve a problem or perform a task.

To be useful and effective, algorithms must possess certain fundamental characteristics:

• Finiteness; they must end after a defined number of steps and must always end.
• Unambiguity; each step must be clearly and unambiguous, because instructions must not be interpreted in different ways.
• Input and output system; algorithms must have an initial input and generate at least one output, i.e., have initial data on which to operate and provide results obtained from instruction execution.
• Determinism; given a specific input, it will always produce the same output through a defined path of steps: there is no element of randomness in the computational process.
• Generality; it must be designed so that it can solve all problems belonging to a particular category or mathematical expression.
• Efficiency, in terms of the time and space required for execution, and thus the ability to solve the problem in the shortest time and with the least use of resources.

Algorithms find application in almost every field imaginable. For example, in software engineering they are fundamental to the development of efficient applications; in the fields of finance, they are used for market forecasting and risk management; in medicine, they support the diagnosis and monitoring of diseases through the analysis of large amounts of clinical data. Algorithms also underpin logistics, optimizing delivery routes and inventory management. And of course, in search engines such as Google, algorithms are central to indexing, ranking, and retrieving information.

Definition of algorithm: meaning and main characteristics

In the standard definition and in general computer science, then, algorithms are a series of well-defined steps to complete a given operation. But what is an algorithm in simple terms? Think of a cooking recipe, where each step must be followed in the correct order to achieve the final dish: similarly, algorithms act as precise guides that tell the computer (or other system) exactly what to do, step by step, to achieve a given goal.

Algorithms can be simple, such as sorting a set of numbers, or extremely complex, such as analyzing large amounts of data to predict market trends. And they can be represented in various ways, such as as pseudo-code, flowcharts or directly in a programming language. This representation allows complex tasks to be translated into a format that can be executed by a computer, automating processes that would otherwise require considerable human effort.

Their importance lies in their ability to automate processes and improve the efficiency of operations, making it possible to handle large amounts of data in reduced time. These concepts-and these systems-are fundamental not only in the theory of computing, but also in the practice of programming.

How many and what algorithms exist and where they are used

Thus, there are countless algorithms, each designed for specific solutions and different applications, and we can classify these systems into different categories depending on their use and the type of problem they solve. In particular, we recognize:

1. Sorting algorithms. Used to organize data in a certain order, for example, the QuickSort algorithm or the Bubble Sort algorithm.
2. Search Algorithms. Used to find an element within a data structure. A well-known example is the binary search algorithm, which repeatedly splits half of the portion of the data to be searched to find the required element.
3. Encryption algorithms. Designed for data security, such as the RSA algorithm for asymmetric encryption or AES for symmetric encryption.
4. Machine learning algorithms. Algorithms used to build systems that can “learn” from data, such as artificial neural networks, linear regression models and decision trees.
5. Genetic algorithms. Inspired by the processes of natural evolution, these algorithms are used to find approximate solutions to complex problems through selection, crossover and mutation techniques.

Why is an algorithm important

Algorithms are crucial in a world increasingly dominated by technology and data: without them, it would be impossible to manage and analyze the immense amount of information available today.

We can say without fear of contradiction that they are the soul of modern technology: they enable extraordinary tasks, from simply ordering a list to predicting future events and understanding natural language, and they increase in centrality as technology evolves and applications become increasingly sophisticated.

Specifically, they are central from the perspective of:

• Efficiency and automation. Algorithms make it possible to automate tasks that would otherwise require enormous human effort, resulting in systems that can operate around the clock without error.
• Speed and scalability. Well-designed algorithms enable problems to be solved very quickly, critical in critical applications such as financial transactions or real-time management of computer networks.
• Reliability and accuracy. Algorithms reduce the likelihood of human error and can be thoroughly tested and verified to ensure accurate results.
• Innovation and progress. Continued research and development of new algorithms pushes forward the boundaries of technology. For example, artificial intelligence algorithms are revolutionizing areas such as medicine, finance, transportation and entertainment.
• Personalization and improvement of user experience. Algorithms used in social media, search engines and e-commerce services improve user experience by offering personalized content and suggestions based on users’ interests and behaviors.

How algorithms work: an overview of the basics

Algorithms are designed to follow a series of well-defined, sequential steps to solve specific problems or perform certain operations. To put it another way, each algorithm starts with a set of input data and proceeds through a series of instructions to produce an output: each step represents a well-defined, sequential instruction that guides the process until the desired result is achieved.

A typical algorithm life cycle begins with defining the problem and identifying the requirements needed to solve it. Various approaches or strategies that could lead to the solution are then designed. Algorithms are then implemented, tested and optimized through an iterative process that can include several steps, including:

• Algorithm design. During this phase, a theoretical framework of the algorithm is developed that details the logical steps required to solve the problem. This phase may include flowcharts and other visual representations to clarify the decision path.
• Coding and implementation. Once designed, the algorithm is translated into code using a specific programming language. This translation must faithfully adhere to the logic defined in the design phase.
• Testing and debugging. After implementation, the algorithm is tested with different input data sets to verify that it produces the expected results. During testing, bugs or inefficiencies may emerge that require correction and optimization.
• Optimization. In this phase, work is done to improve the efficiency of the algorithm by reducing execution time and the use of computational resources. Optimization techniques may include the use of more appropriate data structures or the adoption of more advanced algorithms.

The applications of algorithms: where these systems are found

As mentioned, algorithms are key elements in the operation of modern technologies, offering solutions to complex problems in different domains and constantly improving the efficiency, safety and quality of automated operations. Their mechanism, which transforms inputs into outputs through a structured and repeatable process, ensures consistency and reliability in the results. This ability to automate and standardize operations makes them crucial in numerous applications, which also affect many aspects of our daily lives. For example, when we use social media, an algorithm decides which posts to display in our feed based on our past interests and behaviors; in streaming services such as Netflix, algorithms suggest content based on our viewing preferences. Even in e-commerce, complex algorithms handle everything from product recommendations to secure payment transactions. In each case, the goal is to optimize the user experience and improve operational efficiency.

Examples of algorithms in daily life

Algorithms are therefore ubiquitous and influence numerous activities we perform every day, often without our realizing it.

• Algorithms in Social Media Whenever we access platforms such as Facebook, X or Instagram, algorithms play a crucial role in personalizing our feed. Using machine learning algorithms, these platforms analyze our behaviors, interactions and preferences to show us content that we might be most interested in.

• Algorithms in recommendation systems E-commerce platforms like Amazon and streaming services like Netflix use complex recommendation algorithms to suggest products or content to us. These algorithms analyze our past activities, such as items we have purchased or movies and series we have watched, to predict what we might want in the future.

• Algorithms in road navigation software and map services When we use navigation apps such as Google Maps or Waze, algorithms calculate the optimal route taking into account various factors such as distance, travel time, traffic conditions, and road works.

• Algorithms in smartphone cameras Modern smartphone cameras use image processing algorithms to improve the quality of photos. Features such as autofocus, image stabilization, facial recognition and night mode are all powered by advanced algorithms.

• Algorithms in financial services Algorithms are also widely used in the financial industry. For example, high-frequency trading algorithms are capable of executing millions of trades in the market in seconds, analyzing huge volumes of data to make investment decisions.

• Algorithms in fitness and health applications Many fitness apps, such as MyFitnessPal or Strava, use algorithms to monitor and analyze our physical activities. The algorithms collect data from our smartphone or smartwatch sensors to calculate calories burned, distance traveled, speed, and other performance metrics.

• Algorithms in machine translation systems When we use Google Translate or DeepL, advanced deep learning algorithms and convolutional neural networks work to translate text from one language to another.

• Algorithms in email filtering Machine learning algorithms are widely used in email filtering systems to identify and block spam and phishing.

• Algorithms in virtual assistants Virtual assistants such as Siri, Alexa and Google Assistant use a combination of speech recognition algorithms, natural language processing and machine learning to understand and respond to our requests.

• Algorithms for encryption and security Cryptographic algorithms are critical for protecting sensitive information during transmission and storage. Algorithms such as AES (Advanced Encryption Standard) ensure that data such as credit card numbers, passwords and personal information are rendered unreadable to anyone without the decryption key.

• Algorithms for industrial automation Industrial automation relies on complex algorithms to control machinery and production processes, increasing efficiency and reducing downtime.

• Artificial intelligence algorithms Deep learning algorithms and artificial neural networks underlie many of the most advanced applications of artificial intelligence.

What search engine algorithms are

Deserving separate treatment are search engine algorithms, which are the systems that are triggered every time we do a search on Google, Bing or any other search engine to return us the most relevant results. Search engine algorithms can be defined as the set of rules and procedures used by search engines to determine the relevance and importance of web pages in order to rank them in response to a specific search query.

Their process thus involves crawling web pages, indexing them, and ranking them according to relevance criteria: algorithms evaluate hundreds of factors, such as keywords, content quality, backlinks, and user experience

Come funziona un algoritmo di ricerca

As we know, search engines are essentially answering machines that, prompted by a user’s input, examine billions of pieces of content they have in their repository (the Index) and evaluate thousands of factors to determine which page is most likely to answer the question posed and satisfy the person’s original intent.

In a nutshell, search engine activity consists of three stages:

• Crawling or scanning. Through special programs called “crawlers” or “spiders,” search engines automatically explore the Web and encounter newly available content (Web pages, PDFs, images, videos, and so on): crawlers follow links from one page to another, gathering information about each resource they encounter.
• Indexing. Once a page has been discovered, the information gathered by the crawlers is analyzed and cataloged in a huge database, called the Index, hence the name indexing.
• Ranking or ranking. When a user enters a query into a search engine, the algorithm searches its database for pages that are most relevant to that input: all pages are then sorted or “ranked” based on a number of factors, including content relevance, page authority, quality of inbound links, user experience, and many others.

Therefore, algorithms are used by search engines to analyze and index the content of web pages, and also to support them in determining the relevance and quality of a web page with respect to a user’s search query. Each search engine creates, manages, uses and updates its specific search algorithm.

Google algorithm or algorithms: how many algorithms are there?

At this point it is appropriate to open a small “lexical” parenthesis, but not only. Often, for the sake of simplicity and convenience, we speak of “ Google algorithm” in the singular, even if this means exaggerating a much more complex reality. Google in fact uses a number of algorithms and machine learning techniques to analyze and rank web pages, which work together, each with a specific task, to provide the most relevant and useful search results.

Google’s algorithm is a perfect recipe

Returning to the analogy, there are a multitude of different features of a Web site that are evaluated, “just as we have a number of food items that we get on our plate.” To produce the desired result, “we must have a large number of formulas and sub-algorithms to create each element on the plate and the master algorithm to determine the quantity and placement of each element.”

The role of entities for algorithms

In this context, one element that is receiving increasing attention is the analysis of entities, which we know to be “singular, unique, well-defined and distinguishable things or concepts.”

Ultimately, it is important to understand how algorithms work to add context to what we are experiencing or reading.