What is a recommendation engine, and how it works

Mar 1, 2022

How do recommendation engines work?

Online shopping has grown by leaps and bounds in the past few years. Global e-commerce sales will reach $5 trillion in 2022 and $6 trillion by 2024. As online sellers compete for buyers’ attention, a valuable part of attracting customers and ensuring their loyalty are product recommendations. With the increasing amount of information on the Internet and the significant increase in users, it becomes essential for companies to search, map, and provide them with relevant information according to their preferences. That is where recommendation engines step in and save the day. But what are they, and how do they work? Let’s find out.

A recommendation engine is a system that identifies and delivers recommended content or items to users. When properly configured, it can significantly increase revenue, CTR, and conversions. In addition, product recommendations can also positively affect the user experience, which translates into crucial metrics for online businesses such as customer satisfaction and retention. As mobile applications and other modern technologies continue to change the way users make decisions, recommendation engines are becoming an integral part of various online trading solutions. Before we go over exactly how they work, let’s check the definition of the recommendation engine.

What is a recommendation engine?

The recommendation engine (frequently referred to as a recommender system) is an IT solution that generates product recommendations in real-time, often based on machine learning and other AI technologies. When it comes to large e-commerce sites, it is possible to use more recommendation algorithms. In this case, each engine or algorithm can have a specialized function, such as up-selling or cross-selling. Like most artificial intelligence-based systems, the online merchant recommendation engine can also be used to personalize newsletter offers or suggestions used in retargeting campaigns.

How do recommendation engines work?

A popular example of a recommendation engine is algorithmic software built into music applications such as Pandora and Spotify. Using the recommendation engine is driving a significant change in listening today: Users can connect to a system that selects pieces based on their preferences instead of buying and playing individual songs. Of course, it works the same way in e-commerce, video streaming, and other online services.

The recommendation engine is part of a new field of machine learning where designers create software that tries to learn more about the user and builds a user profile to provide personalized results. That is a part of the field of artificial intelligence, where computers and software systems seem to be increasingly capable of interacting with people intelligently, in this case, by knowing what they want and providing it to them regularly.

The most popular types of recommendation engines

There are three basic types of recommendation engines:

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation systems


Typically, this filtering method involves collecting and analyzing user behavior, actions, or preferences and predicting what they will like based on their similarities to other users. Therefore, collaborative filtering (or community recommendations) takes all visitors’ intentions and preferences into account. That is the most suitable form of the cross-selling recommendation engine. Its advantage is that it follows the trends as its recommendations evolve with users’ interests.


The main benefit of a content-based recommendation system is that it doesn’t require a large user community to be effective. In content-based filtering, recommendations are based solely on what a specific person purchased/viewed in the past. In addition, it is necessary to develop a user profile to indicate the type of item that will be potentially interesting for them.

The fundamental problem with content-based filtering is whether the system will be able to learn user preferences based on their previous actions and adjust to that. There is a risk that such a flawed content-based filtering system could hypothetically recommend the same products all the time, even if they’ve been already purchased. To avoid this problem, online traders have to maintain a certain rigor in completing the information about the features of the products in their databases. Plus, they have to select a solution that deals with this problem effectively, such as RecoAI.


This approach aims to combine the benefits of these two systems mentioned above into a single model to provide organizations with more accurate and better product recommendations. In addition, using a hybrid approach makes it possible to avoid common issues such as so-called cold start or sparsity data problems.

The primary role of recommendation systems is to identify the subgroup to which the user belongs in order to offer suggestions that may interest them. Identifying preferences is usually based on the user’s history of using the service. However, the recommendation system may be based on known user characteristics (age, living, occupation, gender, or education) or a combination of these characteristics and their purchasing history. All that remains is for the recommendation system to find other users who have common traits to analyze the most frequently ordered items, shared or recognized by these users, to offer a personalized selection of recommended products.

Recommendations are used, in particular, by giants such as Amazon or Netflix. Granted, these are huge enterprises, but you can also use recommendation engines in your business as well. Let’s find out where.

Where can you use a recommendation engine?

Personalized recommendations are very widely used in e-commerce but also in other sectors, and the use cases vary significantly. Among many applications, you can mention the following ones:

  • Improving navigation: The engine installed on the e-commerce website can be used to offer complementary products based on the current shopping cart content. For example, if a customer adds a swimsuit to the cart, the engine will be able to offer them sunglasses.
  • Enhancing search results: You can extend the search results on a website with products similar to those searched by the user. Thanks to this approach, it is possible to offer the customer other references when the desired item is unavailable.
  • Cross-selling capabilities: Recommendation systems offer your customers products or services that match their purchases. Here, you can use the suggestions on the website, a sales app, and even email campaigns.
  • Use already created products or services: You can also implement recommendation engines to improve the performance of your sales representatives. By using smart suggesting algorithms, they can respond to calls based on past products or services. In addition, by taking keywords as input, the engine enables shorter reaction times and fewer inquiries to internal experts, who can thus devote themselves to more demanding projects.

As you can see, product recommendations are a powerful tool that allows you to offer users the right products after their first visit to your e-commerce website as soon as the first data is available. Sure, selling online is already rewarding and fascinating, but what if you could do a little more to increase your conversion rate and average order value in your store? That’s why you have us. Contact us today and take advantage of over 20 years of our experience to take your online sales to a new, higher level. At RecoAI, we will create a tailor-made recommendation solution that your users will love.