Recommendation system algorithms: An overview

Apr 12, 2022

Recommendation system algorithm – understand and fulfill your customers’ needs

The success of every online company lies in understanding users’ needs and answering them. One of the ways to achieve that goal is through recommendation algorithms that learn user preferences and, based on that input, generate recommendations that are accurate and tailored to a given person. But how does the recommendation system work? What are recommendation engine algorithms involved? And finally – what’s the end result that you can expect? Let’s find answers to these vital questions.

The importance of recommendation systems is especially visible in the e-commerce sector. In the last year’s edition of Gladly’s CX report, almost 80% of respondents said they highly value personalized service.

Moreover, 91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them1. This means that if you want to win your customers’ engagement, you simply need to provide them with a recommendation system that offers smart product suggestions tailored to each user’s preferences. Of course, such a system requires a lot of information and support from several AI-related technologies. Moreover, there are at least three models to choose from, so the question of finding and implementing the optimal recommender system requires further explanation. And that’s what we’re going to do right now.

How does the recommendation algorithm work?

Generally speaking, these algorithms analyze customer data (e.g., previous purchases, visited subpages, products added to the cart, search logs etc.) and, based on that, show personalized recommendations comprising products and services that have a good chance of interesting a given person.

Typically, the process consists of five stages:

  1. Data collection: Recommendation system should gather all the necessary interactions between users/customers and items. Sometimes even not obvious events like navigating search results or interactions with images should be taken into account.
  2. Data storage: As your online store grows, the amount of available customer data becomes more and more comprehensive. In order for the recommendation system to work, all that data has to be stored for future purposes.
  3. Data analysis: This is where the magic happens. Intelligent recommendation algorithms analyze all the gathered information so that it can be used to provide customers with accurate product suggestions.
  4. Data filtering: Candidates for recommendation are generated using various strategies.
  5. Candidates ranking: In the last stage, machine learning algorithms learn how to rank users

There are three technologies that are indispensable in every recommender system:

  • Machine learning: It’s an AI-related technology that uses diverse models and techniques to assess whether the user is going to like the item that’s being suggested. The more data ML can proceed with, the more accurate it becomes.
  • Natural language processing: NLP is all about enabling the recommender system to “understand” what the user was looking for or what they typed in the search box.
  • Deep learning: DL is a more advanced form of ML that’s capable of analyzing and processing non-linear data (a form of data that doesn’t have a traditional structure like a table or a matrix but something more complex like a list of events or even images or text). Not every recommender system uses this technology, but the ones that do are usually more effective and accurate in their predictions.

Which algorithm is used in recommendation systems?

The first thing you need to know is that recommendation algorithms need to be implemented according to the needs of your business. Recommendation systems are not “one-fits-all” solutions. There are many possible combinations and types of systems that can be implemented. In most cases, we can talk about three major recommendation system algorithms.


It’s the most standard form of a recommendation system algorithm. In content-based filtering, the algorithm uses product features to recommend other items similar to what the user likes, based on their previous purchases. There are some limitations to this system, though. If the given user buys only sporting equipment, the content-based recommendation system will be able to offer other sporting equipment only.


This model is more advanced. Shortly put, collaborative filtering uses similarities between both products and users (simultaneously) to provide recommendations. In other words, the algorithm analyzes the interests and behaviors of users with a similar profile and the items bought in the past by the given person. Then, they suggest new items based on that thorough analysis.

That’s how Netflix works. Its collaborative filtering algorithms use a user movie rating matrix to suggest new movies and TV shows to other subscribers. Let’s see how that works based on a fictional example. There are two users – you and Mark. Mark watched the Netflix show Space Force and liked it very much (he watched the whole season within two days and gave it a 5-star rating). In the past, you and Mark watched and liked a lot of the same movies and shows. Netflix recommendation algorithms discover that Mark liked Space Force, and they suggest it to watch for you, too.


Hybrid recommendation system like described is only one type of mix between various techniques.
The third recommendation system model is simply a combination of the content and collaborative filtering method. In most cases, it’s based on adding the capabilities of the collaborative filtering method to a content-based approach (and vice versa). It can also exploit content and collaborative-based methods to generate separate predictions or combine them into one comprehensive product suggestion.

How to implement a recommendation system algorithm

Here, you have several options. There are some ready-made recommendation platforms that exploit various algorithms to provide your users with product suggestions. However, such algorithms are not always 100% effective as they are ready-made solutions, and they usually take a lot of time and effort to implement. If you’re after something adjusted for your business, its customers and their needs, you need an experienced partner.

We are a company behind an advanced context recommendation system called RecoAI. Our platform is used and appreciated by tons of clients, and it won the 2019 RecSys competition. It still is in 1st place at this prestigious competition! That’s because our system is effective, versatile, and super-fast. We rely heavily on historical data, but when it comes to new users with no history, we use real-time analysis of the user’s behavior so that our algorithms can provide instant suggestions. Recommendations are adjusted within five milliseconds to ensure top-notch accuracy thanks to advanced machine learning algorithms. Furthermore, with RecoAI, recommendations can be different (articles can be recommended to products, products to videos, influencer posts to products, etc.). And don’t worry; we didn’t forget about privacy issues. At no point any sensitive personal data is processed.

To sum up, with our help, you will get a fully functional product recommendation system and comprehensive implementation support. As a result, the implementation process takes just about two weeks. Drop us a line for details.