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:
- Firstly, deep learning dominated computer vision by breakthroughs in classification and segmentation.
- 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.
- 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:
- 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:
- 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.
- 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.