

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.
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During the Open Institute of Technology’s (OPIT) 2025 graduation day, the OPIT team interviewed graduating student Irene about her experience with the MSc in Applied Data Science and AI. The interview focused on how Irene juggled working full-time with her study commitments and the value of the final Capstone project, which is part of all OPIT’s master’s programs.
Irene, a senior developer at ReActive, said she chose to study at OPIT to update her skills for the current and future job market.
OPIT’s MSc in Applied Data Science and AI
In her interview, Irene said she appreciated how OPIT’s course did not focus purely on the hard mathematics behind technologies such as AI and cloud computing, but also on how these technologies can be applied to real business challenges.
She said she appreciated how the course gave her the skills to explain to stakeholders with limited technical knowledge how technology can be leveraged to solve business problems, but it also equipped her to engage with technical teams using their language and jargon. These skills help graduates bridge the gap between management and technology to drive innovation and transformation.
Irene chose to continue working full-time while studying and appreciated how her course advisor helped her plan her study workload around her work commitments “down to the minute” so that she never missed a deadline or was overcome by excessive stress.
She said she would recommend the program to people at any stage in their career who want to adapt to the current job market. She also praised the international nature of the program, in terms of both the faculty and the cohort, as working beyond borders promises to be another major business trend in the coming years.
Capstone Project
Irene described the most fulfilling part of the program as the final Capstone project, which allowed her to apply what she had learned to a real-life challenge.
The Capstone Project and Dissertation, also called the MSc Thesis, is a significant project aimed at consolidating skills acquired during the program through a long-term research project.
Students, with the help of an OPIT supervisor, develop and realize a project proposal as part of the final term of their master’s journey, investigating methodological and practical aspects in program domains. Internships with industrial partners to deliver the project are encouraged and facilitated by OPIT’s staff.
The Capstone project allows students to demonstrate their mastery of their field and the skills they’ve learned when talking to employers as part of the hiring process.
Capstone Project: AI Meets Art
Irene’s Capstone project, “Call Me VasarAI: An AI-Powered Framework for Artwork Recognition and Storytelling,” focused on using AI to bridge the gap between art and artificial intelligence over time, enhancing meaning through contextualization. She developed an AI-powered platform that allows users to upload a work of art and discover the style (e.g. Expressionism), the name of the artist, and a description of the artwork within an art historical context.
Irene commented on how her supervisor helped her fine-tune her ideas into a stronger project and offered continuous guidance throughout the process with weekly progress updates. After defending her thesis in January, she noted how the examiners did not just assess her work but guided her on what could be next.
Other Example Capstone Projects
Irene’s success is just one example of a completed OPIT Capstone project. Below are further examples of both successful projects and projects currently underway.
Elina delivered her Capstone project on predictive modeling of natural disasters using data science and machine learning techniques to analyze global trends in natural disasters and their relationships with climate change-related and socio-economic factors.
According to Elina: “This hands-on experience has reinforced my theoretical and practical abilities in data science and AI. I appreciate the versatility of these skills, which are valuable across many domains. This project has been challenging yet rewarding, showcasing the real-world impact of my academic learning and the interdisciplinary nature of data science and AI.”
For his Capstone project, Musa worked on finding the optimal pipeline to fine-tune a language learning model (LLM) based on the specific language and model, considering EU laws on technological topics such as GDPR, DSA, DME, and the AI Act, which are translated into several languages.
Musa stated: “This Capstone project topic aligns perfectly with my initial interests when applying to OPIT. I am deeply committed to developing a pipeline in the field of EU law, an area that has not been extensively explored yet.”
Tamas worked with industry partner Solergy on his Capstone project, working with generative AI to supercharge lead generation, boost SEO performance, and deliver data-driven marketing insights in the realm of renewable energy.
OPIT’s Master’s Courses
All of OPIT’s master’s courses include a final Capstone project to be completed over one 13-week term in the 90 ECTS program and over two terms in the 120 ECTS program.
The MSc in Digital Business and Innovation is designed for professionals who want to drive digital innovation in both established companies and new digital-native contexts. It covers digital business foundations and the applications of new technologies in business contexts. It emphasizes the use of AI to drive innovation and covers digital entrepreneurship, digital product management, and growth hacking.
The MSc in Responsible Artificial Intelligence combines technical expertise with a focus on the ethical implications of modern AI. It focuses on real-world applications in areas like natural language processing and industry automation, with a focus on sustainable AI systems and environmental impact.
The MSc in Enterprise Cybersecurity prepares students to fulfill the market need for versatile cybersecurity solutions, emphasizing hands-on experience and soft-skills development.
The MSc in Applied Data Science and AI focuses on the intersection between management and technology. It covers the underlying fundamentals, methodologies and tools needed to solve real-life business problems that can be approached using data science and AI.

In May 2025, Greta Maiocchi, Head of Marketing and Administration at the Open Institute of Technology (OPIT), went online with Stefania Tabi, OPIT Career Services Counselor, to discuss how OPIT helps students translate their studies into a career.
You can access OPIT Career Services throughout your course of study to help with making the transition from student to professional. Stefania specifically discussed what companies and businesses are looking for and how OPIT Career Services can help you stand out and find a desirable career with your degree.
What Companies Want
OPIT degrees are tailored to a wide range of individuals, with bachelor’s degrees for those looking to establish a career and master’s degrees for experienced professionals hoping to elevate their skills to meet the current market demand.
OPIT’s degrees establish the foundation of the key technological skills that are set to reshape industries shortly, in particular artificial intelligence (AI), big data, cloud computing, and cybersecurity.
Stefania shared how companies recruiting tech talent are looking for three types of skills:
- Builders – These are the superstars of the industry today, capable of developing the technologies that will transform the industry. These roles include AI engineers, cloud architects, and web developers.
- Protectors – Cybercrime is expected to cost the world $10.5 trillion by the end of 2025, which means companies place a high value on cybersecurity professionals capable of protecting their investment, data, and intellectual property (IP).
- Decoders – Industry is producing more data than ever before, with global data storage projected to exceed 200 zettabytes this year. Businesses seek professionals who can extract value from that data, such as data scientists and data strategists.
Growing Demand
Stefania also shared statistics about the growing demand for these roles. According to the World Economic Forum, there will be a 30-35% greater demand for roles such as data analysts and scientists, big data specialists, business intelligence analysts, data engineers, and database and network professionals by 2027.
The U.S. Bureau of Labor Statistics, meanwhile, predicts that by 2032, the demand for information security will increase by 33.8%, by 21.5% for software developers, by 10.4% for computer network architects, and by 9.9% for computer system analysts. Finally, the McKinsey Global Institute predicts a similar 15-25% increase in demand for technology professionals in the business services sector.
How Career Support Makes a Difference
Next, Stefania explained that while learning essential skills is vital to accessing this growing job market, high demand does not guarantee entry. Today, professionals looking for jobs in the technology field must stand out from the hundreds of applicants for each position with high-level skills.
Applicants demonstrate technical expertise in relevant fields by completing OPIT’s courses. They also need to prove that they can deliver results, demonstrating not just what they know but how they have applied what they know to transform or benefit a business. Professionals also need adaptability, adaptive problem-solving skills, and a commitment to continuous learning. OPIT’s final Capstone projects can be an excellent way to demonstrate the value of newly acquired skills.
Each OPIT program prepares students for future careers by providing dedicated support and academic guidance at every step.
What Kind of Support Does Career Services Offer?
Career Services is specifically focused on assisting students in making the transition to the job market, and you can make an appointment with them at any time during your studies. Stefania gave some specific examples of how Career Services can support students on their journey into the career market.
Stefania said she begins by talking with students and discussing what they truly value to help them discover the type of career that aligns with their strengths. With students who are still undecided on how to start to build their careers, she helps them craft a tailored job and internship search plan.
Stefania has also worked with students who want to stand out during the job application process among the hundreds of applicants. This includes hands-on help in reframing resumes, tailoring LinkedIn profiles, and developing cover letters that tell a unique story.
Finally, Stefania has assisted students in preparing for interviews, helping them research the company, develop intelligent questions about the role to ask the interviewer and engage in mock interviews with an experienced recruiter.
Connecting With Employers
OPIT Career Services also offers students exposure to a wide range of employers and the opportunity to build relationships through masterclasses, career talks, and industry roundtables. The office also helps students build career-ready skills through interactive, hands-on workshops and hosts virtual career fairs with top recruiters.
Career Services also plays an integral role in connecting students with companies for their Capstone project in the final phase of their master’s program. So far, students have worked with companies including Sintica, Cosmica, Cisco, PayPal, Morgan Stanley, AWS, Dylog, and Accenture. Projects have included developing predictive modeling for natural disasters and fine-tuning AI to answer questions about EU tech laws in multiple languages.
What Kinds of Jobs Have OPIT Graduates Secured?
Stefania capped off her talk by sharing some of the positions that OPIT graduates have now fulfilled, including:
- Chief Information Security Officer at MOMO for MTN mobile services in Nigeria
- Data Analyst at ISX Financial in Cyprus
- Head of Sustainability Office at Banca Popolare di Sondrio in Italy
- Data Analyst at Numisma Group in Cyprus
- Senior Software Engineer at Neaform in Italy
OPIT Courses
OPIT offers both foundational bachelor’s degrees and advanced master’s courses, which are both accessible with any bachelor’s degree (it does not have to be in the field of computer science).
Choose between a BSc in Modern Computer Science for a strong technical base or a BSc in Digital Business to focus on applications.
Meanwhile, courses that involve a final Capstone project include an MSc in Applied Data Science and AI, Digital Business and Innovation, Enterprise Cybersecurity, and Responsible Artificial Intelligence.
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