How Does a Recommendation Engine Work? Everything You Need to Know
Personalized recommendation systems have become an indispensable part of every major website. In short, they track and analyze traffic on a website and propose new content or products to visitors.
It is quite likely that thanks to Artificial Intelligence (AI) the customers will be interested in them, which in turn translates into increased sales or viewership on the website.
You’ll learn the following:
- What is a product recommendation system?
- Where are recommender systems used?
- What are the different types of recommendation engines?
- What is the difference between content-based filtering, collaborative filtering, and hybrid filtering?
- What are the benefits of recommendation engines?
What is a recommendation engine?
A product recommendation system is a solution based on an algorithm that allows providing a relevant product to a particular user.
Thanks to the use of artificial intelligence and machine learning technologies, the engine is able to select recommendations in such a way that they will be liked by a specific user with a high mathematical probability.
In other words, the AI recommendation engine generates a personalized set of recommendations that is tailored to the expectations, needs, habits, and interests of the user.
Examples of the use of recommender systems
Currently, such recommendation engines are used in multiple forms on major websites, such as: online movie rentals, streaming services, news websites, online bookstores and many other places online.
The most efficient example of how to use intelligent recommendations is unquestionably the most popular application of the year 2020.
We are obviously talking about TikTok, which has implemented one of the most personalized recommendation engines in the industry.
Thanks to this solution, users do not need to search for what to watch. The Tiktok algorithm does it for them by tailoring the offer to their preferences and habits. TikTok has the highest rate of user engagement as compared to other social media platforms.
Recommendation system in the e-commerce sector
While talking about recommendation systems we cannot skip personalized purchase recommendations that so many online shops are equipped with.
The majority of large e-commerce sites, such as eBay, Amazon or Alibaba uses their own recommendation algorithms to analyze customer behavior and preferences in order to suggest products they may be interested in.
This solution boosts sales, enables cross-selling and increases engagement and average time spent on the website.
Amazon indicates that their recommendation system is responsible for generating up to 35% of the revenue of online shops and the company has recorded 5-fold increase of reactions to recommended products in comparison with their previous, classic solution.
Types of recommendation engines
Depending on the offered products and business profile, e-commerce platforms may configure the recommendation system to get the best conversion.
There are three main approaches to building the basic algorithm that the majority of recommendation models are based on:
1. Content-Based Filtering
Content-based filtering is based on the idea that if a customer likes a given product, there is a high likelihood that they may like another product with similar characteristics.
Example of content-based filtering
YouTube with its video recommendations uses content-based filtering, where a video is considered to be “a product”.
For example, if a user watches various videos with reviews of electronic equipment, the recommended videos tab will contain other materials related to technological novelties.
The content-based filtering system collects information about viewed content by a particular user and starts to recommend further content with a similar theme based on a similar description.
2. Collaborative Filtering
A collaborative filtering method analyzes data obtained from users who bought similar products and then combines this information to create a list of recommended products for the next user.
The great advantage of this method is that it allows you to generate proposals for relatively complicated products such as movies and music with no need for extensive knowledge about them.
There is an underlying assumption that customers give preference to recommendations based on the choices made by other users in the past.
Example of collaborative filtering
For example, if client A viewed Tommy Hilfiger or Calvin Klein T-shirts, client B, who also viewed these products, would see the recommendations based on the viewing history of client A.
The system notices that both clients share similar tastes, so it uses this information to generate product recommendations.
However, it should be borne in mind that the basic aspect of this method is the need to have a large amount of historical data on website traffic in order for the system to efficiently generate recommendations for new users.
3. Hybrid recommendation systems
A hybrid method combines content-based filtering and collaborative filtering. It uses both the information about other users’ behavior in the past as well as preferences of a customer for whom the recommendations are displayed.
In this sense, this method combines the best aspects of the previous two and creates one cohesive solution.
Example of hybrid recommendation engine
An example of a hybrid recommendation system can be seen in the way Spotify selects musical pieces for a personalized “Discover Weekly” playlist.
Users are often struck by how well the songs from those automatically generated playlists meet their musical taste.
The secret of how Spotify can so effectively recommend consecutive songs lies in the complex hybrid filtering system.
The recommendation system collects both the data on the previous users’ habits as well as the users with a similar musical taste.
Thanks to this, Spotify users may enjoy automatically generated playlists which match their musical taste but are still made of songs they have probably never heard before.
How to integrate a recommendation engine with an online shop?
A common denominator of the above-mentioned data filtering methods is the fact that they all use fairly advanced algorithms of Machine Learning to generate product recommendations.
Although those algorithms are sophisticated and complex, integration with an online shop does not need to be complicated.
Recostream is a perfect exemplification of integrating a recommendation system with online stores.
When and why should I use the recommendation engine?
Is implementing a recommendation system in e-commerce shops worthwhile?
Are smaller e-commerce sites going to benefit from implementing advanced systems based on Machine Learning?
Those types of questions are often asked by the owners of e-commerce websites and marketers.
So let's see what are the advantages of the recommendation engine.
Advantages of Recommendation Systems
1. The way to build customer trust and loyalty
It is worth emphasizing that product recommendation systems are one of the most efficient and widely recognized applications of machine learning in business.
When properly configured and implemented, they will boost sales, increase click-through rate as well as customer engagement and other KPIs in every online store.
It results from the fact that customizing product recommendations and content to the preferences of a specific user has a positive impact on user’s experience with a given website
This, in turn, impacts the indicators which are slightly more difficult to measure, but crucial in building a strong brand – namely, customer satisfaction and brand loyalty.
2. An effective way to increase your sales
Recent studies conducted by Monetate show that product recommendations may lead to 70% increase in purchase rates during the initial and subsequent visits of a specific customer. What’s more, product recommendations result in about 33% higher average order values.
Another study carried out by Barilliance showed that product recommendations may result in 31% increase of revenues in online shops when the system if properly optimized and implemented.
Online shop owners with personalized recommendation systems notice that a 12% increase in revenues comes from the sale of products that were recommended to their customers.
You can also observe an increase AOV (average order value). According to the same research done by Barilliance, sessions during which users did not interact with the recommendations in any way were valued at $44.41.
This number increased by 369% when users engaged with recommendations. It is clearly visible in the attached chart:
3. Understanding consumer buying behavior
Another advantage of personalized recommendation systems is the possibility to offer access to a large volume of useful statistics related to website traffic.
Owners of online stores with integrated recommendation systems have a better understanding of customer behavior and tailor the product offer to their needs.
From the user’s point of view, it is much easier and pleasant to navigate through a store with an implemented and efficient recommendation system.
Customers do not need to spend time on browsing through all products available on the website, because those that they may find interesting will be recommended by the recommendation engine.
Summing up, if you are looking for a simple and highly effective way to enhance the personalization of our online store, product recommendation engines may be a good investment. The installation process, regardless of a platform you use, is quick and easy and it does not require programming knowledge. A
A recommendation system will improve the shopping experience by displaying products that the customers would not probably otherwise have come across.