What are recommendation systems?
If you have ever browsed a website displaying products or contents, you’ve most likely come across some type of suggested or recommended items.
Most eCommerce sites have started using this strategy whose ultimate goal is to personalize the product or content offering by showing individual customers the items that better suit their needs, expectations or even tastes. The reason behind this is simple: 74% of customers get frustrated whenever content seems to have nothing to do with what they like.
A recommender system, also known as a recommendation system, uses algorithms to sort information and filter it. Then, it suggests different products or items that are relevant to each individual shopper depending on the context.
Their use is wide and increasingly present on Internet sites. Using data, these recommendation engines will show different content depending on different variables. Among these, we can find past shopping behavior, most recently visited products, and items added to the basket, among others. They can also help large retailers and marketplaces increase the visibility of products in their catalogs.
What type of sites use these systems?
Let’s say you go to Youtube and you watch several videos. Recommendation systems would later show you videos covering the same topic, or that other users who viewed the same video also liked. Or what if you listened to several country songs on Spotify? You might find it interesting to hear similar songs and discover artists you might end up loving. That’s what Spotify recommender systems would do. And that is what they are made for.
They cover many areas and industries. For example, the Badi app helps you find a room and a flatmate using a smart recommendation system powered by AI. The system analyzes user interests, personality and search preferences. Then, it uses this data to discover patterns in features users pay attention to when trying to find the perfect flatmate.
But one of the areas where it has a huge potential is eCommerce. This kind of systems has helped many retailers boost their sales. They use algorithms to analyze customers’ behavior to send out product recommendations, either in product or check-out pages or even in automated promotion emails.
What kind of information does it require?
A recommender system leverages Machine Learning (ML), an area of Artificial Intelligence, to draw conclusions from data. Then, it uses the data to show or suggest customers the products that better match their tastes. But where do we extract that data from? What does it require to be able to closely predict which shoes might a customer like best?
Explicit vs implicit data
Explicit data is information that a customer directly communicates to a website. It can go from the customer’s age or sex to specific comments or opinions on a product. Implicit data, on its turn, is extrapolated from customers behavior on the website. This can go from regularly made purchases, previously seen products, abandoned shopping carts, search history, etc. or even other visited websites or places.
Collaborative vs content-based filtering methods
Essentially, content-based recommendation systems leverage the keywords contained in product data to output recommendations of items a customer might like. This means, that a system like that will recommend products that are similar to the ones previously bought or liked by a user. Their approach is user-specific and they sort the items based on what a specific customer likes or hates about a product. These methods rely heavily on product data, which is why it is essential to have an efficient product content creation strategy in place.
Collaborative filtering methods, on the other side, take into account the behavior of other users—or collective behavior. They might rely, for instance, on item-based filtering, i.e. recommendations of items bought by people who also bought a specific item. Or they can also leverage user-based filtering, in which products are recommended depending on other products a similar user liked.
In the past few years, recommender engines have started to take a combined approach—they mix collaborative and content-based filtering methods in order to nail the best recommendations and increase conversion and the final value of purchases for every customer. Beyond increasing conversion, these recommendations can dramatically improve a site’s UX.
However, recommendations need to be based on large amounts of data and metadata in order to be relevant and improve the user’s experience. The more data they have, the more accurate and relevant recommendations are going to be. Data becomes, therefore, essential.
Using AI to generate the data that powers recommendations
Since data is the fuel that will propel recommendations, we need to find a way to streamline product content generation. Here too, AI comes to the rescue, with auto-generated data based on product image recognition. By only connecting your system to them, AI-assisted product data platforms can analyze your product images using computer vision. Once it recognizes the items, it outputs predictions that help you populate your online catalog.
But what does this exactly mean? Well, it means you can basically let AI populate your catalogs for you with comprehensive data under the form of categories and tags. The result? Increased findability of products and on-point recommendations across your site with less effort from your team.