Machine learning recommendation systems: Introduction

Apr 9, 2022

Recommendation system machine learning – improve the accuracy of your product suggestions

Today, recommendation systems are in a multitude of places and online services. They are common in video streaming platforms, online stores, marketplaces, and even social media platforms. That’s because they make users’ lives easier and help save a lot of time. The accuracy of recommender systems lies in one primary technology – machine learning. In this article, we will show you how recommendation systems work and what role machine learning plays here.

Before we explain how modern recommender systems use machine learning algorithms, let’s talk about the idea for a few moments. You run an online business, perhaps an online store. Why do you need a recommendation system? Does such a solution have any particular impact on your business? The answer is yes. And here’s why.

Why do you need a recommendation system?

In early 2020, Microsoft published an article on 3 prescriptions for subscription success. Here are some of the most interesting findings concerning regular customers that we want to share with you. As it happens, loyal customers are:

  • 5x as likely to repurchase
  • 5x as likely to forgive
  • 4x as likely to refer
  • 7x as likely to try a new offering

That really says a lot, doesn’t it? And the whole point of using a recommendation system is to get more loyal customers! Because once you win user engagement, a relationship begins to form. Recommender systems aid you in nurturing these relationships. And bear in mind, such customers are 7x as likely to try your new offer, i.e., your product recommendation! This means that when you have an effective recommendation system in place, you make more money and close more deals every month.

What can a recommendation system recommend?

Although recommendation systems are rather a B2C thing, it doesn’t mean that B2B companies can’t utilize this solution. It really all depends on the niche that you operate in and… a bit of creativity.

Usually, recommendation systems are used to suggest:

  • Additional services (e.g., express delivery or gift wrapping)
  • Complementary products (that’s what cross-selling is all about)
  • New products that a given user should be interested in
  • New videos, TV shows, movies to watch (as well as books to read)
  • Tools or applications worth trying
  • Content shared by a specific user’s friends or profiles that they liked in the past

Recommendation systems can also work across multiple types of items. For example, it’s possible to recommend e-commerce products in editorials or vice versa.

As you can see, they are quite versatile. And thanks to several AI-related technologies (primarily machine learning), they are effective and accurate, too! Let’s talk about that for a few moments.

How do you create a recommendation system in machine learning?

Creating a recommendation system using machine learning on your own is only for large companies that have the necessary knowledge and resources to deal with such a complex assignment. You have to have at least one person experienced in machine learning, for starters. Then, it would help if you had someone to gather and prepare data for training and analysis purposes. And lastly, you need a team that will put all that together, integrate with your online business, and continually improve the end solution as the amount of available data increases. All in all – quite a lot of work.

Of course, there are available solutions that help you build an ML-based recommendation system from scratch, but it still requires a lot of expertise. These are so-called batch recommendations. One of such solutions is TensorFlow.

However, if you don’t have an in-house machine learning team, you can opt for the second option and work with an external AI company such as RecoAI. We work with online businesses and help them design and implement fully-fledged recommendation systems that are compliant with their development strategies and user needs.

Moreover, our clients frequently need to test what recommendation strategy is optimal (after all, there are at least three models to choose from, we’ll get to that in a moment). A strategy pool tries to select the best strategy for any given scenario. A good example can be a product page. If you have a customer with a non-empty shopping cart – often it is much better to recommend items for the shopping cart instead of showing similar products.

In such a situation, we offer Strategy Builder that works in a drag-and-drop setup. With it, our clients can simulate diverse strategies without the need to put them online. They can choose the option that suits their needs, and once the decision is made, we deploy the final version of your brand new recommendation system.

3 popular recommendation systems with machine learning

Before we show you these three models, let’s talk for a few moments about why machine learning is so important in recommendation systems? You see, decent recommender systems generate recommendations based on what they know about each user. So it all starts with thorough data analysis. Let’s use a simple example. We have an ordinary e-commerce customer; let’s call him Frank. Frank has already made several purchases in your store with electronics. He bought a gaming laptop, a racing wheel, and two car racing games. Based on that input, you can think of other products that he might be interested in, right? For starters, you can offer him every new car racing game in your store. You can also try to suggest to him other genres of games, e.g. shooting games.

You as a human know this because you use a super-advanced brain that processes a lot of information. AI algorithms need to learn that from scratch, and that’s what machine learning is for. With this technology, your recommendation system can understand what each customer is interested in and what products to suggest. The more information they gather, the better they become. As a result, your product suggestions will be more tailored to each user’s needs and interests over time.

There are three basic forms of machine learning recommendation systems. Let’s analyze them more thoroughly.


It’s the most straightforward form using machine learning algorithms. Content-based filtering uses product features only to recommend items similar to what the user liked or bought in the past. So our hypothetical customer Frank will only get suggestions featuring car games and maybe other accessories for car games but no other products related to computer games.


Collaborative filtering is more comprehensive, and it uses similarities between both products and other users to provide product suggestions. So in our Frank example, the algorithm will analyze what he bought in the past, but also what other computer game fans like him were interested in. As a result, the scope of recommended products is much wider.


Probably it’s the most specific type of recommender system that’s based on explicit knowledge related to recommendation criteria. In other words, such a system decides which product should be suggested in which context. So, if your customer bought a racing wheel, the knowledge-based recommender system will instantly recommend purchasing a car racing game, no matter what that person had purchased earlier.

Another type of knowledge-based recommendation can be some kind of an online guide where the client fills out their criteria and the system presents users with the best options to consider (so-called explicit recommendation system).

On the other hand, the first two systems are more implicit. They do not ask the customer about their preferences but assume what they want based on the previous interactions.

The second option could be better for a limited item catalog, but for a catalog with hundreds or thousands of items, building questionnaires for each product category can be very costly. The RecoAI recommendation system is implicit in this sense because it recommends products based on customer interactions (and they can be very diversified; there are 16 interaction types in our database).

If you want to know more about recommendation systems – feel free to reach out. We will gladly help you get to different users in the most effective way!