Driven by the need to personalize user experiences across all touchpoints, businesses are increasingly implementing recommendation engines. These AI-enabled tools can suggest users tailored content, products, and services based on their previous preferences, behavior, demographics, and other relevant data. Estimated at $9.15 billion in 2025, the recommendation engine market is projected to surpass $38 billion by 2030 according to Mordor Intelligence.
In this article, AI developers from Itransition cover the concept of recommender engines, explain how they work, and highlight common use cases of this technology with real-life examples.
How recommendation engines work
Recommendation engines are AI-powered information filtering tools that suggest relevant content, products, or services to users. These tools fall into three major types: content-based filtering engines analyze a user’s past interactions and suggest items similar to those a user previously browsed or liked, collaborative filtering engines identify similarities in the behaviors and preferences of multiple users and suggest items they interacted with to users with similar behaviors or tastes, while hybrid filtering engines combine the elements of both approaches.
Regardless of the engine type, a typical recommendation system works based on the following algorithm. First, implicit user interactions data (such as page views or past purchases) and explicit data (such as comments or likes) is gathered. Then, this data is consolidated for storage in a central repository, such as a database, data lake, or data warehouse, and analyzed by a recommender engine, which identifies items relevant to users and generates suggestions.
Common use cases for recommendation engines
Companies from various industries successfully leverage recommendation engines, and here are some common application areas:
Tailoring ecommerce experiences
Ecommerce recommendation engines can study browsing behavior, purchase histories, and demographic data of visitors to web and mobile online stores and provide them with personalized product suggestions via product carousels, pop-up windows, and other forms of on-site messaging. By offering products that align with customers’ interests and tastes, engines help businesses increase in-store conversions and sales. They also help curate product assortments, alleviating the workloads of merchandising teams.
The example of Sur La Table, a US-based kitchenware seller, illustrates the usefulness of modern recommendation engines. The company decided to equip their ecommerce experience platform with a built-in AI-enabled recommendation engine to provide customers with product suggestions based on customer intent and free up its merchandising team from the need to adjust product rankings manually. As a result, Sur La Table could free merchandisers from the exhausting and time-consuming task and increase customer engagement significantly, driving 1.6 million visits to its “You May Also Like” widget and 1.4 million visits to its “Similar Items” widget.
Delivering tailored media recommendations
Recommendation engines’ capability to predict what content the user is likely to be interested in has also proven instrumental in the media sector. These solutions can analyze implicit and explicit user information, along with data on trending topics in social media and other digital environments, to suggest relevant news articles, music, movies, and other content to online audiences. Companies such as Amazon, YouTube, and Spotify have pioneered the use of recommendation engines in media, and today, an increasing number of media outlets and social media companies follow their steps.
Västerbottens-Kuriren, a Swedish newspaper also known as VK Media, is one of them. Aiming to provide online readers of its news media with more personalized and engaging experiences, the company decided on integrating an AI-powered recommender system into its website. The recommender solution can analyze users’ browsing history to suggest relevant articles from VK Media’s catalog via the “Recommended for you” module on article pages. With its help, the company managed to increase the number of different articles presented to users by 13 times and increase the number of articles clicked by 9 times.
Personalizing training and learning
Learning providers are increasingly implementing recommender engines to suggest more relevant educational materials to consumers based on their stated learning goals, demographic data, and information about past courses or modules they have explored, thereby increasing learner engagement. Companies from various industries also use engines to personalize learning paths for their employees, ensuring more efficient talent development and the mitigation of skill gaps within organizations.
The use of recommender engines in learning is highlighted by many real-life examples. For instance, Booking.com, a travel agency and hotel reservation platform, wanted to offer customized learning experiences to its employees to empower their professional growth. The company also aimed to optimize operations of its educational team, which had to match 15,000 employees with training courses and notify them about new learning opportunities manually. To reach these goals, the company adopted an LMS system complemented by a recommendation engine and integrated it with its corporate HRIS platform.
Based on employee data from the HRIS and data from the learning portal, the engine can recommend relevant training courses to employees, with suggestions delivered to their personalized homepages on the intranet. This helps learners easily find content specific to their roles and reduces the administrative workload on the educational team, who no longer need to handle notifications and other clerical tasks manually. As a result, Booking.com was able to increase the number of corporate learning programs by 30% without increasing the workload of the learning team’s members.
Final thoughts
Recommendation engines are widely and successfully used by companies across various business domains, from ecommerce sales and media distribution to corporate training and development. If you also plan to adopt a recommendation system and are unsure where to start, consider contacting AI development and consulting experts, who can analyze your unique business requirements and develop a tailored project roadmap. If you need technical assistance with development, third-party experts can also help with coding, ML model training, data integration, and other tasks.