Recommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
-
From the same author
Read the articleRecommender systems are AI-based algorithms that use different information to recommend products to customers. We can say that recommender systems are a subtype of machine learning because the algorithms “learn from their past,” i.e., use past data to predict the future.
Today, we’re exposed to vast amounts of information. The internet is overflowing with data on virtually any topic. Recommender systems are like filters that analyze the data and offer the users (you) only relevant information. Since what’s relevant to you may not interest someone else, these systems use unique criteria to provide the best results to everyone.
In this article, we’ll dig deep into recommender systems and discuss their types, applications, and challenges.
Types of Recommender Systems
Learning more about the types of recommender systems will help you understand their purpose.
Content-Based Filtering
With content-based filtering, it’s all about the features of a particular item. Algorithms pick up on specific characteristics to recommend a similar item to the user (you). Of course, the starting point is your previous actions and/or feedback.
Sounds too abstract, doesn’t it? Let’s explain it through a real-life example: movies. Suppose you’ve subscribed to a streaming platform and watched The Notebook (a romance/drama starring Ryan Gosling and Rachel McAdams). Algorithms will sniff around to investigate this movie’s properties:
- Genre
- Actors
- Reviews
- Title
Then, algorithms will suggest what to watch next and display movies with similar features. For example, you may find A Walk to Remember on your list (because it belongs to the same genre and is based on a book by the same author). But you may also see La La Land on the list (although it’s not the same genre and isn’t based on a book, it stars Ryan Gosling).
Some of the advantages of this type are:
- It only needs data from a specific user, not a whole group.
- It’s ideal for those who have interests that don’t fall into the mainstream category.
A potential drawback is:
- It recommends only similar items, so users can’t really expand their interests.
Collaborative Filtering
In this case, users’ preferences and past behaviors “collaborate” with one another, and algorithms use these similarities to recommend items. We have two types of collaborative filtering: user-user and item-item.
User-User Collaborative Filtering
The main idea behind this type of recommender system is that people with similar interests and past purchases are likely to make similar selections in the future. Unlike the previous type, the focus here isn’t just on only one user but a whole group.
Collaborative filtering is popular in e-commerce, with a famous example being Amazon. It analyzes the customers’ profiles and reviews and offers recommended products using that data.
The main advantages of user-user collaborative filtering are:
- It allows users to explore new interests and stay in the loop with trends.
- It doesn’t need information about the specific characteristics of an item.
The biggest disadvantage is:
- It can be overwhelmed by data volume and offer poor results.
Item-Item Collaborative Filtering
If you were ever wondering how Amazon knows you want a mint green protective case for the phone you just ordered, the answer is item-item collaborative filtering. Amazon invented this type of filtering back in 1998. With it, the e-commerce platform can make quick product suggestions and let users purchase them with ease. Here, the focus isn’t on similarities between users but between products.
Some of the advantages of item-item collaborative filtering are:
- It doesn’t require information about the user.
- It encourages users to purchase more products.
The main drawback is:
- It can suffer from a decrease in performance when there’s a vast amount of data.
Hybrid Recommender Systems
As we’ve seen, both collaborative and content-based filtering have their advantages and drawbacks. Experts designed hybrid recommender systems that grab the best of both worlds. They overcome the problems behind collaborative and content-based filtering and offer better performance.
With hybrid recommender systems, algorithms take into account different factors:
- Users’ preferences
- Users’ past purchases
- Users’ product ratings
- Similarities between items
- Current trends
A classic example of a hybrid recommender system is Netflix. Here, you’ll see the recommended content based on the TV shows and movies you’ve already watched. You can also discover content that users with similar interests enjoy and can see what’s trending at the moment.
The biggest strong points of this system are:
- It offers precise and personalized recommendations.
- It doesn’t have cold-start problems (poor performance due to lack of information).
The main drawback is:
- It’s highly complex.
Machine Learning Techniques in Recommender Systems
It’s fair to say that machine learning is like the foundation stone of recommender systems. This sub-type of artificial intelligence (AI) represents the process of computers generating knowledge from data. We understand the “machine” part, but what does “learning” implicate? “Learning” means that machines improve their performance and enhance capabilities as they learn more information and become more “experienced.”
The four machine learning techniques recommender systems love are:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep learning
Supervised Learning
In this case, algorithms feed off past data to predict the future. To do that, algorithms need to know what they’re looking for in the data and what the target is. The data in which we know the target label are named labeled datasets, and they teach algorithms how to classify data or make predictions.
Supervised learning has found its place in recommender systems because it helps understand patterns and offers valuable recommendations to users. It analyzes the users’ past behavior to predict their future. Plus, supervised learning can handle large amounts of data.
The most obvious drawback of supervised learning is that it requires human involvement, and training machines to make predictions is no walk in the park. There’s also the issue of result accuracy. Whether or not the results will be accurate largely depends on the input and target values.
Unsupervised Learning
With unsupervised learning, there’s no need to “train” machines on what to look for in datasets. Instead, the machines analyze the information to discover hidden patterns or similar features. In other words, you can sit back and relax while the algorithms do their magic. There’s no need to worry about inputs and target values, and that is one of the best things about unsupervised learning.
How does this machine learning technique fit into recommender systems? The main application is exploration. With unsupervised learning, you can discover trends and patterns you didn’t even know existed. It can discover surprising similarities and differences between users and their online behavior. Simply put, unsupervised learning can perfect your recommendation strategies and make them more precise and personal.
Reinforcement Learning
Reinforcement learning is another technique used in recommender systems. It functions like a reward-punishment system, where the machine has a goal that it needs to achieve through a series of steps. The machine will try a strategy, receive back, change the strategy as necessary, and try again until it reaches the goal and gets a reward.
The most basic example of reinforcement learning in recommender systems is movie recommendations. In this case, the “reward” would be the user giving a five-star rating to the recommended movie.
Deep Learning
Deep learning is one of the most advanced (and most fascinating) subcategories of AI. The main idea behind deep learning is building neural networks that mimic and function similarly to human brains. Machines that feature this technology can learn new information and draw their own conclusions without any human assistance.
Thanks to this, deep learning offers fine-tuned suggestions to users, enhances their satisfaction, and ultimately leads to higher profits for companies that use it.
Challenges and Future Trends in Recommender Systems
Although we may not realize it, recommender systems are the driving force of online purchases and content streaming. Without them, we wouldn’t be able to discover amazing TV shows, movies, songs, and products that make our lives better, simpler, and more enjoyable.
Without a doubt, the internet would look very different if it wasn’t for recommender systems. But as you may have noticed, what you see as recommended isn’t always what you want, need, or like. In fact, the recommendations can be so wrong that you may be shocked how the internet could misinterpret you like that. Recommender systems aren’t perfect (at least not yet), and they face different challenges that affect their performance:
- Data sparsity and scalability – If users don’t leave a trace online (don’t review items), the machines don’t have enough data to analyze and make recommendations. Likewise, the datasets change and grow constantly, which can also represent an issue.
- Cold start problem – When new users become a part of a system, they may not receive relevant recommendations because algorithms don’t “know” their preferences, past purchases, or ratings. The same goes for new items introduced to a system.
- Privacy and security concerns – Privacy and security are always at the spotlight of recommender systems. The situation is a paradox. The more a system knows about you, the better recommendations you’ll get. At the same time, you may not be willing to let a system learn your personal information if you want to maintain your privacy. But then, you won’t enjoy great recommendations.
- Incorporating contextual information – Besides “typical” information, other data can help make more precise and relevant recommendations. The problem is how to incorporate them.
- Explainability and trust – Can a recommender system explain why it made a certain recommendation, and can you trust it?
Discover New Worlds with Recommender Systems
Recommender systems are growing smarter by the day, thanks to machine learning and technological advancements. The recommendations were introduced to allow us to save time and find exactly what we’re looking for in a jiff. At the same time, they let us experiment and try something different.
While recommender systems have come a long way, there’s still more than enough room for further development.
Read the articleTens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).
But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.
It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.
The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.
Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.
History of Natural Language Processing
Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.
Early Developments in NLP
Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?
In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.
Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.
Evolution of NLP Techniques and Algorithms
Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:
- 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
- 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
- 1980s – IBM developed an array of NLP-based statistical solutions.
- 1990s – Recurrent neural networks took center stage.
The Role of Artificial Intelligence and Machine Learning in NLP
Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.
AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.
Key Components of Natural Language Processing
The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.
Syntax Analysis
An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”
Semantic Analysis
Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.
Pragmatic Analysis
A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.
Discourse Analysis
When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.
Speech Recognition and Generation
Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.
Natural Language Processing Techniques
Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.
Rule-Based Approaches
Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.
Statistical Methods
If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.
Neural Networks and Deep Learning
You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.
Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.
Transfer Learning and Pre-Trained Language Models
The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.
Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.
Applications of Natural Language Processing
With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:
- Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
- Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
- Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
- Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
- Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.
Challenges and Limitations of Natural Language Processing
Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:
- Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
- Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
- Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
- Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.
The Future of Natural Language Processing
The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?
- Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
- Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
- Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
- Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?
The Potential of Natural Language Processing Is Unrivaled
It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.
The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.
If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.
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During the Open Institute of Technology’s (OPIT’s) 2025 Graduation Day, we conducted interviews with many recent graduates to understand why they chose OPIT, how they felt about the course, and what advice they might give to others considering studying at OPIT.
Karina is an experienced FinTech professional who is an experienced integration manager, ERP specialist, and business analyst. She was interested in learning AI applications to expand her career possibilities, and she chose OPIT’s MSc in Applied Data Science & AI.
In the interview, Karina discussed why she chose OPIT over other courses of study, the main challenges she faced when completing the course while working full-time, and the kind of support she received from OPIT and other students.
Why Study at OPIT?
Karina explained that she was interested in enhancing her AI skills to take advantage of a major emerging technology in the FinTech field. She said that she was looking for a course that was affordable and that she could manage alongside her current demanding job. Karina noted that she did not have the luxury to take time off to become a full-time student.
She was principally looking at courses in the United States and the United Kingdom. She found that comprehensive courses were expensive, costing upwards of $50,000, and did not always offer flexible study options. Meanwhile, flexible courses that she could complete while working offered excellent individual modules, but didn’t always add up to a coherent whole. This was something that set OPIT apart.
Karina admits that she was initially skeptical when she encountered OPIT because, at the time, it was still very new. OPIT only started offering courses in September 2023, so 2025 was the first cohort of graduates.
Nevertheless, Karina was interested in OPIT’s affordable study options and the flexibility of fully remote learning and part-time options. She said that when she looked into the course, she realized that it aligned very closely with what she was looking for.
In particular, Karina noted that she was always wary of further study because of the level of mathematics required in most computer science courses. She appreciated that OPIT’s course focused on understanding the underlying core principles and the potential applications, rather than the fine programming and mathematical details. This made the course more applicable to her professional life.
OPIT’s MSc in Applied Data Science & AI
The course Karina took was OPIT’s MSc in Applied Data Science & AI. It is a three- to four-term course (13 weeks), which can take between one and two years to complete, depending on the pace you choose and whether you choose the 90 or 120 ECTS option. As well as part-time, there are also regular and fast-track options.
The course is fully online and completed in English, with an accessible tuition fee of €2,250 per term, which is €6,750 for the 90 ECTS course and €9,000 for the 120 ECTS course. Payment plans are available as are scholarships, and discounts are available if you pay the full amount upfront.
It matches foundational tech modules with business application modules to build a strong foundation. It then ends with a term-long research project culminating in a thesis. Internships with industry partners are encouraged and facilitated by OPIT, or professionals can work on projects within their own companies.
Entry requirements include a bachelor’s degree or equivalency in any field, including non-tech fields, and English proficiency to a B2 level.
Faculty members include Pierluigi Casale, a former Data Science and AI Innovation Officer for the European Parliament and Principal Data Scientist at TomTom; Paco Awissi, former VP at PSL Group and an instructor at McGill University; and Marzi Bakhshandeh, a Senior Product Manager at ING.
Challenges and Support
Karina shared that her biggest challenge while studying at OPIT was time management and juggling the heavy learning schedule with her hectic job. She admitted that when balancing the two, there were times when her social life suffered, but it was doable. The key to her success was organization, time management, and the support of the rest of the cohort.
According to Karina, the cohort WhatsApp group was often a lifeline that helped keep her focused and optimistic during challenging times. Sharing challenges with others in the same boat and seeing the example of her peers often helped.
The OPIT Cohort
OPIT has a wide and varied cohort with over 300 students studying remotely from 78 countries around the world. Around 80% of OPIT’s students are already working professionals who are currently employed at top companies in a variety of industries. This includes global tech firms such as Accenture, Cisco, and Broadcom, FinTech companies like UBS, PwC, Deloitte, and the First Bank of Nigeria, and innovative startups and enterprises like Dynatrace, Leonardo, and the Pharo Foundation.
Study Methods
This cohort meets in OPIT’s online classrooms, powered by the Canvas Learning Management System (LMS). One of the world’s leading teaching and learning software, it acts as a virtual hub for all of OPIT’s academic activities, including live lectures and discussion boards. OPIT also uses the same portal to conduct continuous assessments and prepare students before final exams.
If you want to collaborate with other students, there is a collaboration tab where you can set up workrooms, and also an official Slack platform. Students tend to use WhatsApp for other informal communications.
If students need additional support, they can book an appointment with the course coordinator through Canvas to get advice on managing their workload and balancing their commitments. Students also get access to experienced career advisor Mike McCulloch, who can provide expert guidance.
A Supportive Environment
These services and resources create a supportive environment for OPIT students, which Karina says helped her throughout her course of study. Karina suggests organization and leaning into help from the community are the best ways to succeed when studying with OPIT.
In April 2025, Professor Francesco Derchi from the Open Institute of Technology (OPIT) and Chair of OPIT’s Digital Business programs entered the online classroom to talk about the current state of the Metaverse and what companies can do to engage with this technological shift. As an expert in digital marketing, he is well-placed to talk about how brands can leverage the Metaverse to further company goals.
Current State of the Metaverse
Francesco started by exploring what the Metaverse is and the rocky history of its development. Although many associate the term Metaverse with Mark Zuckerberg’s 2021 announcement of Meta’s pivot toward a virtual immersive experience co-created by users, the concept actually existed long before. In his 1992 novel Snow Crash, author Neal Stephenson described a very similar concept, with people using avatars to seamlessly step out of the real world and into a highly connected virtual world.
Zuckerberg’s announcement was not even the start of real Metaverse-like experiences. Released in 2003, Second Life is a virtual world in which multiple users come together and engage through avatars. Participation in Second Life peaked at about one million active users in 2007. Similarly, Minecraft, released in 2011, is a virtual world where users can explore and build, and it offers multiplayer options.
What set Zuckerberg’s vision apart from these earlier iterations is that he imagined a much broader virtual world, with almost limitless creation and interaction possibilities. However, this proved much more difficult in practice.
Both Meta and Microsoft started investing significantly in the Metaverse at around the same time, with Microsoft completing its acquisition of Activision Blizzard – a gaming company that creates virtual world games such as World of Warcraft – in 2023 and working with Epic Games to bring Fortnite to their Xbox cloud gaming platform.
But limited adoption of new Metaverse technology saw both Meta and Microsoft announce major layoffs and cutbacks on their Metaverse investments.
Open Garden Metaverse
One of the major issues for the big Metaverse vision is that it requires an open-garden Metaverse. Matthew Ball defined this kind of Metaverse in his 2022 book:
“A massively scaled and interoperable network of real-time rendered 3D virtual worlds that can be experienced synchronously and persistently by an effectively unlimited number of users with an individual sense of presence, and with continuity of data, such as identity, history, entitlements, objects, communication, and payments.”
This vision requires an open Metaverse, a virtual world beyond any single company’s walled garden that allows interaction across platforms. With the current technology and state of the market, this is believed to be at least 10 years away.
With that in mind, Zuckerberg and Meta have pivoted away from expanding their Metaverse towards delivering devices such as AI glasses with augmented reality capabilities and virtual reality headsets.
Nevertheless, the Metaverse is still expanding today, but within walled garden contexts. Francesco pointed to Pokémon Go and Roblox as examples of Metaverse-esque words with enormous engagement and popularity.
Brands Engaging with the Metaverse: Nike Case Study
What does that mean for brands? Should they ignore the Metaverse until it becomes a more realistic proposition, or should they be establishing their Meta presence now?
Francesco used Nike’s successful approach to Meta engagement to show how brands can leverage the Metaverse today.
He pointed out that this was a strategic move from Nike to protect their brand. As a cultural phenomenon, people will naturally bring their affinity with Nike into the virtual space with them. If Nike doesn’t constantly monitor that presence, they can lose control of it. Rather than see this as a threat, Nike identified it as an opportunity. As people engage more online, their virtual appearance can become even more important than their physical appearance. Therefore, there is a space for Nike to occupy in this virtual world as a cultural icon.
Nike chose an ad hoc approach, going to users where they are and providing experiences within popular existing platforms.
As more than 1.5 million people play Fortnite every day, Nike started there, first selling a variety of virtual shoes that users can buy to kit out their avatars.
Roblox similarly has around 380 million monthly active users, so Nike entered the space and created NIKELAND, a purpose-built virtual area that offers a unique brand experience in the virtual world. For example, during NBA All-Star Week, LeBron James visited NIKELAND, where he coached and engaged with players. During the FIFA World Cup, NIKELAND let users claim two free soccer jerseys to show support for their favorite teams. According to statistics published at the end of 2023, in less than two years, NIKELAND had more than 34.9 million visitors, with over 13.4 billion hours of engagement and $185 million in NFT (non-fungible tokens or unique digital assets) sales.
Final Thoughts
Francesco concluded by discussing that while Nike has been successful in the Metaverse, this is not necessarily a success that will be simple for smaller brands to replicate. Nike was successful in the virtual world because they are a cultural phenomenon, and the Metaverse is a combination of technology and culture.
Therefore, brands today must decide how to engage with the current state of the Metaverse and prepare for its potential future expansion. Because existing Metaverses are walled gardens, brands also need to decide which Metaverses warrant investment or whether it is worth creating their own dedicated platforms. This all comes down to an appetite for risk.
Facing these types of challenges comes down to understanding the business potential of new technologies and making decisions based on risk and opportunity. OPIT’s BSc in Digital Business and MSc in Digital Business and Innovation help develop these skills, with Francesco also serving as program chair.
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