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May 31, 2019

Product Recommendation System: Algorithms, Challenges, Benefits


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

In the modern world providing users with a personalized experience is virtually a must. They adore feeling special and understanding that someone takes care of them. That’s why they like those recommendations that much. Such an experience simply shows that your website, app or online store appreciates each of them in particular. This is how product recommendation system influences that your customers stay loyal, while more and more users join your service.

That’s logical and clear, but here come the most important questions. How to reach this goal and move from standard user experience to the personalized one? What techniques to use? Are there any potential challenges? And can such a change guarantee any other benefits apart from personalized experience and loyal users? Yes, there are a lot of questions but don’t worry, we will answer all of them. Just keep reading, and discover what to do in order to make your business more successful and popular.

Product Recommendation Systems: definition and goal

Let us start with the most basic things: the definition of the product recommendation systems. A bit of theory will provide you with a better understanding of things we will be talking about later. So, first of all, there is no difference between a product recommendation system and a recommender system[1]. These terms sound a bit different, but they refer to the same things. To avoid confusion, we will use the first version — a product recommendation system. It is a software tool which main mission is generating suggestions for products or content a particular user would like to buy or to check.

A product recommendation system works using diverse machine learning techniques (we will tell more about them in the next paragraph) and relevant data. A data set should include information both about individual users and products. On the basis of this data, a system develops an entire network of connections between those clients and the products you offer.

The primary goal of building such a system is to simplify your clients’ search of products or content. It narrows down the variety of choices, so people can focus only on those products which they are really interested in. Apart from this, there are some other benefits you can get with the implementation of a product recommendation system. But, again, we will provide you with more information about them a bit later.


Machine Learning product recommendation system techniques

Now, when you know the basics, let us explain to you what machine learning techniques can be used in these systems.

Content-based filtering

Such a system defines what products users click and buy, what pages they view, etc. Then, on the basis of this information, a user profile is created. Compared to the product list, the profile is used to provide recommendations.

Collaborative filtering

This technique implies using recommendations from users. Their behavior and preferences are analyzed and then used to determine similarities between users. As a result, you will understand which products a particular user may like thanks to their similarity to other customers.

Complementary filtering

The complementary filtering technique analyzes the probability of a few products being purchased together. In other words, it defines complementary products of a specific item. When a user buys this item, they get a recommendation to purchase a complementary one.

Demographic-based filtering

Clearly, this technique uses demographic information of the customers. It recommends products bought by users with a similar demographic profile.

Hybrid recommendation systems

There is no need to focus on a single technique. To get better results and more accurate suggestions, you can combine several of them in a hybrid system.

Building a product recommendation system: challenges you may face

The first challenge you may face is processing huge data sets to get real-time predictions. AI Consulting is a great help, but you will still have to set up the parameters. The larger the data set is, the harder it will be to reach the maximum accuracy. Use large-scale assessment methods to overcome the task.

The second issue is that your system will have no information about new users. Thus, it won’t be able to recommend something to them on the basis of their profiles and preferences. To solve this problem, you can recommend popular products or use contextual information (for instance, the user’s location). New products also have no reviews or clicks for some time, until users discover them. You can recommend such products using their metadata and the content-based filtering technique.

And, finally, the diversity. Collaborative methods are effective, but sometimes they can’t deliver sufficient diversity. To deal with this issue, you can recommend products disliked by people who are not similar to a specific user.

The Benefits A Product Recommendation System Can Bring

Implementing a product recommendation system is definitely worth all the potential difficulties and challenges. And here are three reasons to prove this statement.

Improved user experience

Just imagine: you have already bought a bicycle online. Now you want to purchase some accessories, spare parts or tools (from the same website), but you don’t know exactly what you may need. You don’t even know which spare parts will fit your transport! A website offers nothing to you, so you have to do everything on your own. You waste an hour trying to deal with this task, but in the end, you simply go on your bicycle to a shop. You find a consultant there, and with their help buy all the stuff. Everything is fine, but you spend loads of time and effort on solving a simple problem. Will you ever go back to that online store to buy anything else? We doubt this.

The same can happen to your clients if they don’t get personal recommendations. When your website or app recommends them what to purchase, they understand that you take care of them. They enjoy your service, they are happier. And happy customers are exactly what you need. In turn, wasting time on checking all the products can be irritating for users. And if they have to do this every time they want to buy something, they can simply leave at some point.

Increased sales

With an improved user experience comes the second benefit — increased sales. According, to Monetate Report, using a product recommendation system can lead to a 70% increase in sales, and that’s a lot. Customers make purchases even in the case initially they didn’t know what to buy. Let’s go back to our example with a bicycle. A person buys one, and when they come back to the shop, the system offers spare tires and a set of tools. Both these products perfectly fit that particular model. Will a person continue shopping and buy those products? We can’t give you a 100% guarantee, but we can assure you that they will at least think about that. And some of them will definitely make a purchase.

Higher retention rate

Happy customers knowing that they are important to you are much more likely to stay loyal to your service. This applies both to already existing customers and those who will join you in the future. And, actually, happy customers can really help you to increase customer retention. If they like your website or app, they can simply recommend it to their friends and family. In this way, you will get more users who will make more purchases. Obviously, this leads to higher profits.

Now you know much more about product recommendation systems than before reading this article. So, it is time to put this knowledge into practice and make your application, website, or online store more attractive. However, in case you still have any questions, you are welcome to ask them. Just get in touch with us, and we will quickly get back to you.


[1] Wikipedia. Recommender system. URL: Accessed May 31, 2019.


Machine Learning