Recommendation Systems have become one of the most dynamic areas of digital transformation. Companies worldwide are turning to artificial intelligence to accelerate decision-making, optimize processes, and deliver more personalized customer experiences. For many organizations, this technology is the ideal starting point in their AI journey — it can be implemented quickly and often generates measurable business results within just a few months.
Recommendation Systems – More Than Just Online Sales
When we hear “recommendation system,” most people think of Amazon or Netflix suggesting products or movies. In reality, recommendations go far beyond that: the optimal delivery route for a truck driver, predictive maintenance for a factory machine, or clinical decision support for doctors. The principle remains the same — an algorithm analyzes data and suggests the best option, which a human (or another system) can accept or reject.
Recommendation Systems and the Quick Impact of MVP
Experts emphasize that recommendation systems are the perfect entry point into the AI world. An MVP (minimum viable product) can be built in just 2–3 months from the moment the clean and processed data is gathered. The costs are relatively low, while the benefits are immediate: higher conversion rates in e-commerce (such as an increase in click-through rate (CTR)), increased average order value (AOV), improved customer retention, or reduced delivery times in logistics. It’s worth noting that the first version of a recommendation system often delivers the strongest “boost,” while further development and maintenance become a long-term process.
Types of Recommendation Systems
Collaborative Filtering
Recommendations based on user similarity (e.g., based on demographics). The drawback is the cold start problem: new products cannot be recommended until someone has interacted with them. Additionally, the cold start problem affects not only new products but also new users, for whom the system has no data yet. There is also product-based collaborative filtering: customers who bought X also bought Y.
Content-Based Filtering
Analyzes product features (e.g., movie genres, machine parameters). It solves the cold start issue for products but risks creating a recommendation bubble — similar to listening to the same genre of music over and over: it’s enjoyable, but it doesn’t introduce anything new.
Hybrid Systems
Combine both approaches and are most commonly used in scalable applications.
Challenges: Data, Information Drift, and Explainable AI
The most common barrier for companies is the lack of data. Without proper datasets, the system has nothing to learn from. Equally problematic is collecting data “just in case,” without a strategy — this often leads to the big data trap and unnecessary costs.
Deployed models also require continuous monitoring to respond to concept drift — changes in customer trends, seasonality, or demographics. A system that worked perfectly last year may already be outdated today.
Another crucial factor is explainable AI. Users are more likely to trust recommendations if they understand why they received them — for example, “other customers bought this product” or “this movie is trending in your country.” Transparency strengthens both loyalty and a sense of security.
Practical Applications in Business
Recommendation systems are not limited to technology giants. They prove effective across multiple industries:
- E-commerce – smart suggestions increase the average cart value.
- Media & Streaming – personalization boosts viewership and time spent on the platform.
- Logistics – route recommendations or warehouse optimization reduce costs and delivery times.
- Manufacturing – predictive maintenance prevents failures and costly downtime.
- Healthcare – therapeutic recommendations support doctors in clinical decision-making while keeping the human role central.
Case Study: AI in Practice
Stermedia has delivered many projects where recommendation mechanisms were the key success factor. One example is a partnership with a logistics company — algorithms analyzing hundreds of possible routes recommended the most optimal options for drivers. The result? Faster deliveries, fuel savings, and improved customer satisfaction.
Another project involved optimizing an AI platform for a US-based technology company. Stermedia not only took over and improved the existing solution but also transformed it into a scalable tool that recommends the best decisions in daily business processes. This shows that recommendations can apply not only to shopping but also to strategic business choices.
Emerging Trends in Recommendation Systems
The future of recommendation systems is tied to greater personalization and improved data quality management. Increasingly, companies combine internal data with external sources such as market trends to react faster to global changes.
Equally important is ensuring ethics and fairness. Controversial examples — such as Microsoft’s failed chatbot or the Target algorithm that predicted customer pregnancies — demonstrate that AI requires responsibility and a well-thought-out strategy.
Summary
Recommendation systems are one of the best ways to begin your journey with artificial intelligence. The ability to quickly build an MVP, deliver fast results, and expand into broader applications makes them accessible not only to global corporations but also to mid-sized enterprises.
If you want to explore how recommendation systems can increase your business efficiency, learn more about Stermedia’s solutions.
