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/

What is a recommendation engine, and how it works

What is a recommendation engine, and how it works

How do recommendation engines work?

Online shopping has grown by leaps and bounds in the past few years. Global e-commerce sales will reach $5 trillion in 2022 and $6 trillion by 2024. As online sellers compete for buyers’ attention, a valuable part of attracting customers and ensuring their loyalty are product recommendations. With the increasing amount of information on the Internet and the significant increase in users, it becomes essential for companies to search, map, and provide them with relevant information according to their preferences. That is where recommendation engines step in and save the day. But what are they, and how do they work? Let’s find out.

A recommendation engine is a system that identifies and delivers recommended content or items to users. When properly configured, it can significantly increase revenue, CTR, and conversions. In addition, product recommendations can also positively affect the user experience, which translates into crucial metrics for online businesses such as customer satisfaction and retention. As mobile applications and other modern technologies continue to change the way users make decisions, recommendation engines are becoming an integral part of various online trading solutions. Before we go over exactly how they work, let’s check the definition of the recommendation engine.

What is a recommendation engine?

The recommendation engine (frequently referred to as a recommender system) is an IT solution that generates product recommendations in real-time, often based on machine learning and other AI technologies. When it comes to large e-commerce sites, it is possible to use more recommendation algorithms. In this case, each engine or algorithm can have a specialized function, such as up-selling or cross-selling. Like most artificial intelligence-based systems, the online merchant recommendation engine can also be used to personalize newsletter offers or suggestions used in retargeting campaigns.

How do recommendation engines work?

A popular example of a recommendation engine is algorithmic software built into music applications such as Pandora and Spotify. Using the recommendation engine is driving a significant change in listening today: Users can connect to a system that selects pieces based on their preferences instead of buying and playing individual songs. Of course, it works the same way in e-commerce, video streaming, and other online services.

The recommendation engine is part of a new field of machine learning where designers create software that tries to learn more about the user and builds a user profile to provide personalized results. That is a part of the field of artificial intelligence, where computers and software systems seem to be increasingly capable of interacting with people intelligently, in this case, by knowing what they want and providing it to them regularly.

The most popular types of recommendation engines

There are three basic types of recommendation engines:

  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation systems

COLLABORATIVE FILTERING

Typically, this filtering method involves collecting and analyzing user behavior, actions, or preferences and predicting what they will like based on their similarities to other users. Therefore, collaborative filtering (or community recommendations) takes all visitors’ intentions and preferences into account. That is the most suitable form of the cross-selling recommendation engine. Its advantage is that it follows the trends as its recommendations evolve with users’ interests.

CONTENT-BASED FILTERING

The main benefit of a content-based recommendation system is that it doesn’t require a large user community to be effective. In content-based filtering, recommendations are based solely on what a specific person purchased/viewed in the past. In addition, it is necessary to develop a user profile to indicate the type of item that will be potentially interesting for them.

The fundamental problem with content-based filtering is whether the system will be able to learn user preferences based on their previous actions and adjust to that. There is a risk that such a flawed content-based filtering system could hypothetically recommend the same products all the time, even if they’ve been already purchased. To avoid this problem, online traders have to maintain a certain rigor in completing the information about the features of the products in their databases. Plus, they have to select a solution that deals with this problem effectively, such as RecoAI.

HYBRID FILTERING

This approach aims to combine the benefits of these two systems mentioned above into a single model to provide organizations with more accurate and better product recommendations. In addition, using a hybrid approach makes it possible to avoid common issues such as so-called cold start or sparsity data problems.

The primary role of recommendation systems is to identify the subgroup to which the user belongs in order to offer suggestions that may interest them. Identifying preferences is usually based on the user’s history of using the service. However, the recommendation system may be based on known user characteristics (age, living, occupation, gender, or education) or a combination of these characteristics and their purchasing history. All that remains is for the recommendation system to find other users who have common traits to analyze the most frequently ordered items, shared or recognized by these users, to offer a personalized selection of recommended products.

Recommendations are used, in particular, by giants such as Amazon or Netflix. Granted, these are huge enterprises, but you can also use recommendation engines in your business as well. Let’s find out where.

Where can you use a recommendation engine?

Personalized recommendations are very widely used in e-commerce but also in other sectors, and the use cases vary significantly. Among many applications, you can mention the following ones:

  • Improving navigation: The engine installed on the e-commerce website can be used to offer complementary products based on the current shopping cart content. For example, if a customer adds a swimsuit to the cart, the engine will be able to offer them sunglasses.
  • Enhancing search results: You can extend the search results on a website with products similar to those searched by the user. Thanks to this approach, it is possible to offer the customer other references when the desired item is unavailable.
  • Cross-selling capabilities: Recommendation systems offer your customers products or services that match their purchases. Here, you can use the suggestions on the website, a sales app, and even email campaigns.
  • Use already created products or services: You can also implement recommendation engines to improve the performance of your sales representatives. By using smart suggesting algorithms, they can respond to calls based on past products or services. In addition, by taking keywords as input, the engine enables shorter reaction times and fewer inquiries to internal experts, who can thus devote themselves to more demanding projects.

As you can see, product recommendations are a powerful tool that allows you to offer users the right products after their first visit to your e-commerce website as soon as the first data is available. Sure, selling online is already rewarding and fascinating, but what if you could do a little more to increase your conversion rate and average order value in your store? That’s why you have us. Contact us today and take advantage of over 20 years of our experience to take your online sales to a new, higher level. At RecoAI, we will create a tailor-made recommendation solution that your users will love.