Recommendation systems are one of the popular applications of Data Science in every domain. The recommendation system of Amazon works by using both collaborative filtering and content-based filtering techniques. Amazon uses collaborative filtering to analyze user behaviour and recommend products favoured by customers with similar preferences. It also employs content-based filtering to extract attributes and suggest products with similar features. In this article, I’ll take you through a complete user journey while looking for products at Amazon to explain how Amazon Recommendation System works.
Here’s How Amazon Recommendation System Works
Let’s understand how the recommendation system of Amazon works, let’s start by looking for a product to understand how Amazon recommends products in the journey of a user at Amazon while looking for a product.
I started by looking for a watch for myself. Now, let’s say I liked this watch shown below:
Below this watch, amazon is recommending me more products in two sections:
- “What do customers buy after viewing this item?”;
- “Compare with similar items”;
Section-1: What do customers buy after viewing this item?
These are post-purchase recommendations or related product recommendations.
Post-purchase recommendations are suggestions for additional products that Amazon displays to customers based on the behaviour of other customers who have viewed the same item. These recommendations fall under the category of collaborative filtering, which is a popular recommendation system technique. Collaborative filtering relies on finding patterns in multiple users’ behaviour to make predictions about what a specific user might like.
In this case, Amazon analyzes the historical data of other customers who have interacted with the watch I am viewing. It identifies customers who have not only viewed the same watch but also made subsequent purchases. The system then examines the other products these customers have bought after viewing the watch and compiles a list of the most frequently purchased items. These items are presented as “What do customers buy after viewing this item?” recommendations.
Section-2: Compare with similar items
These are known as item similarity recommendations.
Item similarity recommendations aim to show products that share similar attributes, features, or characteristics with the product you are currently viewing. Item similarity recommendations are a type of content-based filtering. Unlike collaborative filtering, it focuses on the attributes and features of the items to make recommendations.
In this case, the Amazon recommendation system analyzes the product’s metadata and identifies its key attributes, such as brand, model, design, features, and specifications. It then looks for other watches in the same category or with similar attributes. Then the system presents these “Compare with similar items” recommendations to provide you with various options within the same product category.
So, Amazon employs collaborative filtering to analyze user behaviour, tracking browsing history and product interactions to identify customers with similar preferences. It then recommends products favoured by users with comparable behaviour, showcased in sections like “What do customers buy after viewing this item?”. Additionally, Amazon utilizes content-based filtering, extracting attributes and metadata from products to create user profiles based on interactions and preferences. This approach allows the system to suggest products with similar features and specifications in the “Compare with similar items” section.
You can learn more about recommendation systems practically from the resources mentioned below:
- Building a Hybrid Recommendation System (Collaborative + Content Based)
- Free course on Recommendation Systems by Google Developers
So this is how the recommendation system of Amazon works. Amazon Recommendation System works by using collaborative filtering to analyze user behaviour and recommend products favoured by customers with similar preferences. It also employs content-based filtering to extract attributes and suggest products with similar features. The combination of these techniques enables personalized and diverse product recommendations, enhancing the user’s shopping experience. I hope you liked this article on how Amazon’s recommendation system works. Feel free to ask valuable questions in the comments section below.