The Mechanics Behind Amazon’s Recommendation System

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Amazon’s recommendation system is renowned for its ability to offer customers personalised and relevant product recommendations. It uses data analytics and other advanced technologies to deliver tailored suggestions.



Collaborative Filtering
Amazon’s recommendation system primarily works on collaborative filtering techniques. Amazon analyses a large amount of data, including user behaviour and preferences, to make product recommendations. It also analyses a customer’s purchase history and their browsing behaviour and ratings. Here, the goal is to identify patterns and similarities among different users. When Amazon finds users with similar tastes and preferences, it can recommend the products one customer has liked to others who have similar interests.

Content-Based Filtering
Amazon uses content-based filtering to analyse the characteristics of a product, including south korea phone number list their titles, categories, descriptions, specifications, etc. Amazon then recommends similar products based on the features and characteristics of each product by understanding the content of each product.

Machine Learning and Deep Learning
Amazon pulls insights from product descriptions, customer reviews, and other textual data by using NLP techniques. For example, if a customer has left a positive review for a product, Amazon’s recommendation engine can identify keywords to recommend similar products to the customer.