Data Science: how to create a recommendation system with machine learning

By Jordi Zaragoza, published on June 6, 2023

If you have an ecommerce this interests you. First I want you to imagine browsing platforms like Amazon, Netflix or Spotify. Surely you will see many suggestions about products that may interest you, movies or series that you may like or music of your style. Well, these recommendations are not random. They are part of the so-called recommender systems with data sciencewhich many companies implement because they report a large number of benefits.

In this article we are going to bring you a little closer to this world and teach you how to implement it step by step.

What are recommender systems?

Recommender systems are algorithms that try to predict which products or services from an online store are more likely to be purchased by a user to then show them on the web while you browse.

Before the birth of machine learning, what ecommerce did to show the consumer interesting articles was to add lists of “the most purchased” or “the best valued”. However, these types of sections showed all users the same articles or services. Although they are still used recommender systems have been shown to be more effective at making personalized suggestions which are different for each client.

How do recommendation systems work?

Recommender systems are based on data analysis based on the information that has been collected from user browsing, such as what products they have viewed or purchased and how they have interacted with the platform.

For this, they use advanced algorithms capable of making detailed comparisons between different user profiles and finding common patterns. Thanks to this, they are able to recommend increasingly relevant products or services for each particular consumer.

Types of recommenders

When creating recommendation systems, experts can use two types of strategies:

• Recommenders with collaborative filters: the algorithm bases its logic on the user’s own characteristics and the information it collects from the user becomes the center. In this case, previous purchases are taken into account, the ratings you have given to products, the average cost per purchase, preferences, etc.. Then, he looks for other similar users who have made similar decisions and detects what products or services they liked and then recommends them to him.
• Recommenders with content-based filters: in this case, it is not the person that is the basis of the prediction, but the product or service. The user’s purchase characteristics are not taken into account, the characteristics of the product are attended to (price, brand, rating, size…) to make the recommendation.

Why implement recommendation systems in your ecommerce?

• They increase the chances that a consumer will make an additional purchase.
• They maximize the overall sales of the company.
• They retain customers for longer in the online store.
• They boost consumer satisfaction by recommending products that interest them.
• The chances that a customer will be loyal are higher with this system.

When not to implement a recommendation system with machine learning

Despite all the benefits of implementing recommendation systems, it may not be the best thing for your business at this time. If you still have few clients, or if your catalog of products or services is small, the algorithm that you are going to be able to develop will not be very useful for you. Investing in data science is more profitable the more clients you have and the greater offer of products or services .

How to create a recommendation system with machine learning

Python is one of the most used languages ​​to create data science and machine learning tools and also when creating web pages, mainly due to its robust code and optimized syntax. Programmers starting out in this world are recommended to use it, since it is one of the most reliable languages ​​when it comes to creating software.

However, there are also other alternatives such as Java, Golang, Node.js, PHP or Ruby.

Java is the best alternative to Python, in fact it is its main competitor so to speak.

If you want to implement the recommendation system of your website or improve the one you already have, our data science team can help you. Contact us if you want us to analyze your situation without obligation.

Tips to improve your recommendation systems

location matters

Where and when the recommendations appear in your ecommerce matters. In addition, getting these two aspects right can be decisive not only for the recommendation system to really work, but also for the user experience.

In this sense, these elements cannot be generalized, because depending on your website and the type of products or services, one place and one time will be better than another. However, the most common in ecommerce is that the recommendations appear at the bottom of the article that the user is viewing or at the end of the purchase process.

If you are not sure, we recommend that you do A/B tests to make the best decision.

Give a strategic sense to your recommendations

What is a good recommendation? Are all the ones that are good for the customer good for your business? Well, the truth is that not always.

If we put ourselves in the shoes of the consumer, there are recommendations that are too obvious to be useful. Thus, it can be interesting to make risky recommendations that introduce the customer to unknown products or services.

From a business standpoint, it’s critical to base recommendations on product profitability, so this wouldn’t be a bad approach to start with.

Due to all this, making a balance between what is good for you and what is good for the client would be the key.

We hope we have helped you to learn more about the world of recommendation systems and encourage you to include it in your e-commerce (if you don’t already have it) or to improve it with the tips and tricks that we have given you here.