What Is a Recommender System? How To Build One
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Recommender systems shape many of the digital experiences we take for granted today, from the products we see on e-commerce sites to the shows suggested on streaming platforms. At a basic level, a recommender system analyzes user behavior, preferences, and patterns to predict what a person is most likely to engage with next. This primer breaks down how recommender systems work, the different types you should know, and why they matter for modern businesses. You will learn how approaches like collaborative filtering, content-based recommendations, and hybrid models help brands personalize experiences at scale.
What is a recommender system?
A recommender system is a compelling information filtering system running on machine learning (ML) algorithms that can predict a customer’s ratings or preferences for a product.
A recommendation engine helps to address the challenge of information overload in the e-commerce space.
Modern recommender systems were created first by e-commerce giants like Amazon and then popularized by OTT platforms like Netflix.
But now you might encounter recommendations in almost every space of the digital world, be it Facebook Posts, Insta Stories, YouTube videos, to food delivery services, and e-commerce business.
With the introduction of cost-effective and easily available software, more and more enterprises can now develop and deploy their recommender systems.
Building A Recommender System
Building recommendation engines: One software that Express Analytics uses in developing recommenders engine for clients is the Neo4j software. This is a graph database management system, unlike traditional RDBMS.
The Neo4j foundation is on “Nodes”, “Relationship”, and “Properties”. A “Node” is a data or record in a graph database while a “Relationship” is an element using which two nodes of a graph can be connected.
This relationship creates a pattern that associates two chunks of information together and defines a flow by assigning a direction to it. We can add pieces of information to our nodes and relationships by assigning properties to them. But more on the Neo4j later.
Just to make it easy to understand it better, for this blog post, we will help explain how we design a recommendation engine for an e-commerce client.
3 Types of Recommender Systems
Before going ahead with the explainer on how to build a recommendation engine, let us learn some of the various types:
Recommendation systems can be classified into three categories:
- Content-based filtering
- Collaborative Filtering
- Hybrid
Recommendations can be made based on the customer’s recent purchase history, demographics, and gender.
What is Content based Recommendation System?
Recommendations through content-based filtering techniques are influenced by what the user has browsed earlier, or what he is currently browsing. It is mostly dependent on keyword searching techniques and tries to follow the user’s own browsing patterns by filtering items based on keywords to describe the items and make recommendations around them.
What is Collaborative Filtering Recommender Systems?
Unlike content-based filtering which only takes into account user-specific item interactions, the collaborative filtering technique follows a more mature approach and finds out similar users based on user-item interactions.
What is Hybrid Recommendation System?
Once you mix concepts of content-based filtering and collaborative filtering to generate recommendations, you have developed a hybrid system.
So, let’s commence building a recommendation engine for a hypothetical e-commerce firm.
Consider the above graph data structure. Here, we have four nodes: State, Customers, Category, and Products, and three relationships with directions connecting the four nodes. Their respective properties are in the assigned dialogue type box.
We want to recommend products to the customers. For this purpose, we will develop some algorithms to make the recommendations.
Data alone does not drive your business. Decisions do.
Making Recommendations More Dynamic
Now let us make some more adjustments to the query to make our recommendations more dynamic.
The recommendation of the above type comes under the “Content-based filtering technique”, where we have used filtering on the customer’s browsing pattern to find the recommendations.
Recommendation based on the Product A Customer is Searching for in Real-Time
Another content-based recommendation algorithm that we can develop is based on finding out products similar to the one being browsed at the moment.
User-user Collaborative Filter-based Recommendations
Here the idea is to use the customer’s profile and make recommendations purely based on his/her preference and liking.
User-user collaborative filtering aims at finding customers who are similar to you based on the products you both are browsing or by comparing the ratings you both give to a particular product.
This is where the Neo4j software proves to be superior to others as it provides some different similarity algorithms. To name a few — “Jaccard Similarity” and “Cosine Similarity” are mostly used in this type of collaborative filtering.
Let’s take a breather here. In the 2nd part of this post, we will show you how to use the Neo4j platform for building a recommender system, a comparison between the Neo4j, the MongoDB, and MySQL, and various use cases of recommendation engines.