Recommendation system algorithms: An overview

Recommendation system algorithms: An overview

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.

CONTENT-BASED RECOMMENDATION SYSTEM

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.

COLLABORATIVE FILTERING RECOMMENDATION SYSTEM

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

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.

 

  1. https://www.forbes.com/sites/blakemorgan/2020/02/18/50-stats-showing-the-power-of-personalization/?sh=3994f5a52a94
Machine learning recommendation systems: Introduction

Machine learning recommendation systems: Introduction

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.

CONTENT-BASED FILTERING

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

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.

KNOWLEDGE-BASED RECOMMENDER SYSTEMS

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!

Netflix recommendation system: How it works

Netflix recommendation system: How it works

With over 220 million subscribers, Netflix is one of the most popular streaming platforms featuring hundreds of movies, animations, and TV shows. What’s the secret behind their success? Netflix users value this platform for a wide offer, high-quality subtitles, and, yes, accurate, personalized recommendations. Netflix exploits an advanced machine-learning-based recommendation system that analyzes users’ choices to suggest them new movies and TV shows. In this article, we are going to take a closer look at their recommendation engine and see what lessons other businesses can draw from their system.

Netflix began back in 1997 as a movie rental service that allowed customers to order movies and receive them via snail mail. In 2000, Netflix started using data science and analytics tools to recommend videos for users to rent1. In 2006 Netflix organized a $1 million challenge to improve their recommendations2. Although the solutions turned out to be too difficult to implement in practice, this challenge sparked huge innovations in the area of smart product recommendations. For example, it was demonstrated for the first time that a matrix factorization is a viable approach to recommendations3.

Their approach was successful and now, they are a leading video streaming platform.

Did you know that about 80%4 of what people watch on Netflix comes from their recommendation algorithms? That’s the best proof of their efficiency. Plus, Netflix dynamically gets more and more users5 (almost 222 million subscribers as of Q4 2021), which also shows that they are doing a great job understanding peoples’ needs concerning entertainment.

And since the Netflix recommendation system is their secret weapon, it’s definitely work taking a closer look at it.

What is the Netflix recommendation system?

In essence, it’s a set of algorithms using machine learning to analyze user data and movie ratings. To make it more effective, Netflix has set up 1,300 recommendation clusters based on users viewing preferences. As a result, whenever you turn on Netflix, you see a list of movies and TV shows tailored to your interests and user profile. The goal is to help each user find a movie or a TV show they will enjoy and do so as quickly as possible (estimations are they have just 90 seconds for that). The recommendation system itself remains in the back end and is invisible to the user.

How does their recommendation system work?

Netflix’s system shows personalized recommendations based on several factors, including6:

  • Each user’s previous interactions (e.g., viewing history, searches, and ratings)
  • Other members’ choices (especially those with similar tastes and preferences)
  • Information about the specific title (genre, category, the year of release, etc.)
  • The device used to watch videos on Netflix
  • The watching time

Combined, all that data is a useful input for Netflix recommendation algorithms. They process and analyze all that data and use machine learning to turn it into useful and accurate movie recommendations. But let’s suppose that you’ve never used this platform. What does it look like in the beginning?

When a user creates a new Netflix account, they have to choose several movie titles that they like. It’s a starting point for the recommendation algorithm to start working. As time goes by and you watch more movies and more TV shows, the recommendation system learns your habits and preferences. As a result, the suggestions that you get are becoming more and more accurate. Additionally, the recommender system used by Netflix focuses on each user’s most recent choices. So if a few years ago you were interested in fantasy movies, but now you mainly watch romance movies, the algorithm will suggest mostly such titles.

You have to know that Netflix does so much more than just provide you with personalized recommendations. In fact, everything you see on the homepage is tailored towards your preferences. Their recommendation system ranks titles in a way that is designed to present them in the best possible order. This means that:

  • There are several rows of suggestions containing different titles for you to watch
  • Each title is ranked within the row
  • Each thumbnail is also tailored to a specific user (sometimes, when you log into the system, you see how these thumbnails instantly change; now you know why)

TWO-TIERED RANKING SYSTEM

Netflix uses an effective two-tiered approach to movie recommendations. Here’s how it works:

It’s all optimized so that the user would benefit from straightforward navigation and most probable movie suggestions. Of course, there can be millions of ways of presenting content this way. That’s one of the challenges they have to address every day. As we can read on Netflix TechBlog7:

“To algorithmically create a good personalized homepage means assembling one page per member profile and device from thousands of videos that may be relevant for a member and from easily tens of thousands of potential rows, each with a variable number of videos.”

What algorithms does Netflix use?

The Netflix recommendation system is actually very complex, and it uses various technologies and machine learning models to provide millions of users with accurate suggestions. There are several algorithmic approaches in place, and they comprise8:

  • Reinforcement learning (RL algorithms don’t need any information in advance; they learn from data during the process)
  • Neural networks (they try to imitate the way the human cortex works; neural networks are extremely important in deep learning)
  • Causal modelling (it’s an analytical technique concentrated on the cause-and-effect relationships)
  • Probabilistic graphical models (PGM expresses the conditional dependence structure between random variables)
  • Matrix factorization (it’s a class of collaborative filtering algorithms used specifically in recommendation systems)
  • Ensemble learning (a technique using multiple learning algorithms to achieve better results)

As you can see, the Netflix recommendation system is far more complex than it would seem to an average user! Moreover, the vital part of their recommender system relies on A/B testing. They constantly test various options concerning movie suggestions, thumbnails, and how titles are organized to determine what triggers the biggest interest and engagement. For instance, in case of a viewer who likes romantic movies, the artwork personalization can mean that such a person will see a thumbnail presenting a romantic aspect of the movie. You can read more about that on Netflix blog.

The lesson for you

Today, Netflix has huge amount of users and interactions. This means that they can rely on simpler methods that scale well with those numbers. Of course, Netflix’s system is not perfect. But they tirelessly work on developing and improving their product suggestions. That’s why, at least partly, why people adore this platform. You should go the same way. If your business could use a recommendation system, don’t neglect that part of the business. Start with a well-thought-out strategy and gather as much data about your customers as possible. Next, use all that information to devise a tailor-made system. There is no copy-paste in recommendation systems; everything should be adjusted to the needs of the given business. And that’s why you need a professional AI partner.

Work with RecoAI

If you want to benefit from a recommendation system in your company, drop us a line! We’ve been operating in this niche for some time now and with great success. Our team has won several competitions on Kaggle (their challenges constitute a true milestone in the development of such systems). Additionally, we won the 2019 RecSys competition.

At RecoAI, we specialize in intelligent recommender systems fueled by AI and machine learning. We will gladly help you get the most of the data that your company processes to reach users more effectively.

1. https://medium.com/@springboard_ind/how-netflixs-recommendation-engine-works-bd1ee381bf81
2. https://en.wikipedia.org/wiki/Netflix_Prize
3. https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf
4. https://www.youtube.com/watch?v=f8OK1HBEgn0
5. https://www.statista.com/statistics/250934/quarterly-number-of-netflix-streaming-subscribers-worldwide/
6. https://help.netflix.com/en/node/100639/us
7. https://netflixtechblog.com/learning-a-personalized-homepage-aa8ec670359a
8. https://medium.com/@springboard_ind/how-netflixs-recommendation-engine-works-bd1ee381bf81

E-commerce: Product recommendation system. Why you should have it?

E-commerce: Product recommendation system. Why you should have it?

Product recommendation system for e-commerce

Amazon gets 30% of its revenue from recommendation systems only. That perfectly explains why product recommendations are so important in e-commerce. What do you need to know about them? And why in modern online trade product recommendation engines are indispensable? That’s what we’re going to discuss today.

When you think about growing an e-commerce business, it all revolves around customer satisfaction. The satisfied customers buy more products (which, in turn, increases average order value), and eagerly return to place more orders. Customers that are taken care of usually become regular ones and constitute a vital part of your business performance.

In the Monetate Ecommerce Quarterly Report (Q2 2018), we can read that “in sessions in which customers either buy recommended products or add them to their carts, average order value jumps 33% (compared to sessions in which customers only see recommended products, but don’t engage with them)1. Amazon’s results are similar.

What’s the lesson here? You have to make sure your product recommendations are as effective as possible. It’s the only way to encourage customers to view them and buy suggested products. It all starts with adopting a proper strategic mindset.

The way of thinking of product recommendations in e-commerce

The key to boosting e-commerce business always lies in understanding user preferences and providing them with accurate product suggestions. In fact, these suggestions should welcome your customers the second they enter the store.

However, you have to understand that trust is always the necessary foundation. And what can cause your customers to lose trust in you? According to a study conducted by Adobe, there are three main behaviors that you have to avoid2. Here’s what Adobe respondents said about the stores they don’t trust:

  1. They are creepy (49%): “They track me online without my permission, send emails, but I do not remember giving them my information.”
  2. They are annoying (39%): “They send me too many communications, they are not clear about their privacy policies or what they do with my data.”
  3. They do not listen (39%): “They keep sending me ads or communications, even after I have opted out.”

If your product recommendations deliver a poor experience and focus on forcing customers to buy and not to make them happy and come back next time – you’re in dire straits. We advise you to choose a different approach. Think about making your customers happy and turning your product recommendation engine into an actual shopping assistant that’s helpful, not annoying.

Moreover, recommendations should always be helpful and adjusted to your best knowledge about customers. We often see e-commerce websites presenting their bestselling products wherever they can. That’s not the best strategy. In fact, in many situations, presenting irrelevant recommendations can ruin customers’ trust. The homepage is a good place for presenting bestsellers if you do not know anything about the customer, but as soon as the customer searches for something, their activity should be taken into consideration.

What is a product recommendation system for an e-commerce store?

In e-commerce, product recommendation algorithms gather customer data (e.g., history of purchases, products added to the cart, viewed product categories, etc.) and analyze it to provide them with product recommendations tailored to a given person’s needs. The more advanced the recommendation system you have, the more effective and accurate its suggestions are.

In e-commerce, recommendations can be displayed in different places and at different stages of the purchasing process. Here are a few examples of where you can put product suggestions:

  • In the product tab (a perfect place to make the most of upselling and cross-selling)
  • In the cart (before the customers finish their orders, show them some complementary products that they can find useful)
  • In the thank-you email/message (even after the order is placed, you can offer other relevant products based on customer data)
  • In the subsequent email campaigns (some customers can decide to buy more products even weeks after placing the first order)

In general, there are three recommendation engines that you can use. Usually, they all rely on machine learning. This AI-related technology allows your recommendation algorithms to improve over time as they have more and more data to process, but it’s all about the approach. Let’s see what your options are.

3 most important recommendation engines

CONTENT-BASED FILTERING

This form of product filtering uses data about the items to suggest similar or complementary products to goods that a given user purchased or viewed in the past. There are some limitations to this model, though. The algorithm will only provide suggestions related to products that are similar to those already purchased or interacted with.

COLLABORATIVE FILTERING

Here, the algorithm uses similarities between both products and other users to provide product suggestions. As a result, they can also offer products purchased by other customers with similar profiles or interests.

HYBRID RECOMMENDATION SYSTEM

Lastly, we have a combination of content-based filtering and collaborative filtering methods. It’s the way our RecoAI system works. In most cases, hybrid recommendation systems are based on adding the capabilities of the collaborative filtering method to content-based filtering. The goal, of course, is to provide users with the most accurate suggestions.

The benefits of a product recommendations engine

What are the benefits of a tailor-made product recommendation engine? Let’s have a look at them:

  • Increased loyalty: Customers that get useful tips from you will be more open to your suggestions and will eagerly return to buy more products.
  • Higher CTR: Well-thought-out product suggestions are highly clickable, which helps your business and SEO.
  • Ability to use advanced selling techniques: Product recommendations are just perfect for upselling and cross-selling!
  • A future-proof solution: The e-commerce sector grows dynamically (retail e-commerce sales are predicted to reach 7.4 trillion dollars by 20253). There will be more and more online stores and more customers buying there.
  • Better UX: Personalized product recommendations make people feel important, and that positively affects their UX.

image source: Gladly.com CX report 2020

Build your new recommendation engine with RecoAI!

What makes our solution worth your attention? We’ve made sure that our recommendation engine uses the best machine-learning-based algorithms and a modern and quick codebase built with the Rust programming language. As a result, our systems are up to four times faster than competitive systems.

Moreover, our systems are not limited just to products. We analyze and adapt every single element on your website to make sure your store works like a well-oiled machine. With RecoAI, recommendations can be very different (articles to products, products to videos, influencer posts to products, etc.). We rely heavily on historical data, but when it comes to new customers with no purchasing history, we use real-time analysis of the shopper’s behavior so that our algorithms can provide instant suggestions based on each user’s actions and preferences.

Do you want to know more? Grow user engagement with RecoAI! Drop us a line, and we’ll take it from there.

1. https://info.monetate.com/rs/092-TQN-434/images/EQ2_2018-The-Right-Recommendations.pdf
2. https://blog.adobe.com/en/publish/2021/11/04/7-in-10-customers-will-buy-more-from-brands-they-trust-uk

Deep learning-based recommendation system

Deep learning-based recommendation system

What do you need to know about deep learning-based recommender systems?

Recommendation systems use several advanced AI-related technologies. Most of them are based on traditional machine learning methods. However, sometimes, this approach is insufficient. Deep learning is your best bet when you need a more complex solution. In this article, we are going to talk about deep learning techniques and how they are useful in deep learning-based recommender systems.

We frequently talk about machine learning and recommender systems using traditional machine learning models on our blog. But what about deep learning? What is this technology all about? Let’s find out. But first, let’s talk for a few moments about recommendation systems themselves.

Recommendation system: What it is and how it works

In today’s online world, recommender systems are absolutely indispensable. They help companies using them attract more customers, close more deals, increase UX, and improve customer service. In general, these systems are used to suggest products (or services) that might interest a particular customer – 100% automatically.

Let’s see how they work in the e-commerce world. Suppose you run an online store. There are hundreds of products in your offer, and thousands of customers are browsing them every day. Their actions constitute a valid source of information. Some users will be interested in product X, while others in Y. Understanding these correlations effectively means that you can create new product suggestions based on your observations.

Today, you don’t have to do that manually. Recommender systems that are fueled with AI do that for you. They analyze your offer and track the behavior of your customers. Based on that, these algorithms learn which customers are interested in some products and which in others. That’s, in short, how recommendation systems work. But they must learn how users behave in order to provide them with useful information. The technology enabling that process is called machine learning. But today, we’re interested in deep learning. So, what’s the difference?

What is deep learning?

By its most straightforward definition, deep learning is the more advanced form of machine learning. Generally speaking, traditional machine learning models need to be taught (trained) what they are supposed to do and how. So, if you’re working on a machine-learning-based recommender system, you need to teach these algorithms what your products are, what your customers are, and how all of that is interconnected. Once these algorithms are trained with a sufficient amount of data, they can discern whether a particular user should be interested in a specific product, e.g., based on their previous purchases (so-called content-based filtering) or interests of other people with similar profiles (collaborative filtering).

With deep learning, it’s a different story. Here, the algorithms can determine on their own whether the outcome (in our example, product recommendation) is accurate. That’s possible thanks to so-called neural networks (we’ll get to that). Let’s take a quick jump in time and see how deep learning evolved:

  1. Firstly, deep learning dominated computer vision by breakthroughs in classification and segmentation.
  2. In the last 3 years it has also dominated NLP (natural language processing), but also tasks like machine translation, text understanding and even text generation.
  3. New architectures like transformers model are a serious contender against more traditional approaches.

THE IMPORTANCE OF DEEP LEARNING-BASED RECOMMENDER SYSTEMS

You have to know that the e-commerce world evolves rapidly. And so is the entire IT/AI sector. Customer preferences change, and there is more data about them available. There was simply the need for a system that would be capable of collecting all that information to provide customers with more accurate, tailor-made recommendations.

Additionally, today, we have multiple segmentation and personalization options. Customers expect that the store or the website will communicate with them personally and understand their preferences. Advanced AI-based solutions like deep learning enable just that.

And thirdly, in some situations, traditional machine learning models are technically insufficient. Here’s a quick example: Amazon. According to data from 2021, Amazon sells over 12 million products1. As a result, the number of item-user interactions will be quite low (it’s difficult to imagine a customer willing to display all 12 million products and purchase, say 3 million of them, correct?) – it’s the so-called data sparsity problem. And here’s another issue – product recommendations for new customers who haven’t taken any significant action yet so that the machine learning algorithm didn’t have a chance to learn that new person’s preferences. It’s the cold start problem.

All in all – that’s why there was a need for a more advanced system. That said, let’s take a look at how deep neural networks work in recommendation systems.

Deep neural networks for recommendation systems

Deep neural networks constitute a super-advanced multi-layered model that structures AI algorithms in layers. The goal is to create a structure that functions similarly to the human brain. Therefore, there is no need for extensive initial training (like it happens with traditional machine learning models), and deep learning algorithms are capable of capturing non-linear patterns in datasets. This means that deep learning-based recommender systems are better at dealing with non-standard interaction patterns and, as a result, understanding and reflecting users’ preferences more accurately. When it comes to recommender systems, it is important to mention three areas:

  1. Image and text understanding help in the content-based approaches redefining items’ similarities. One of the examples is search based on visual similarity of the products based on deep learning vision models. Take a look at this can look like in the system provided by RecoAI:
  2. The same can be said about the text. Deep learning models help in gaining the understanding of the products’ titles and descriptions and also user queries when they search for a specific product. Similarly to computer vision models, they can also identify similar products more accurately.
  3. Third aspect in deep learning recommendations are new deep learning frameworks that try to replace the core recommendation algorithms. One such example is recently announced Facebook library called PyTorch.

Build your recommendation system with RecoAI

At RecoAI, we help clients all over the world create effective and accurate recommendation systems using top-notch AI algorithms. Whether you run an online store or other forms of online business, our solution is for you. Our system is based on the advanced Rust programming language that enables us to build recommender systems that are up to four times faster than competitive solutions.

Do you want to know more? Go to our website and send the contact form. We’ll take care of the rest.

1. https://www.bigcommerce.com/blog/amazon-statistics/