From Data to Decisions: How Amazon’s Item-to-Item Algorithm Transformed Recommendation Systems
Introduction to Recommendation Systems Recommendation systems have become a fundamental component of modern digital platforms. They help users discover relevant products, movies, music, and other content based on their preferences and past interactions. Large e-commerce platforms such as Amazon use recommendation algorithms to personalize the shopping experience and improve customer engagement. However, building an efficient recommendation system becomes increasingly difficult as the number of users and items grows. Handling millions of users and products requires algorithms that can generate accurate recommendations while maintaining high computational efficiency. Challenges in Traditional Recommendation Methods Many early recommendation systems relied on Collaborative Filtering, a technique that predicts a user’s interests based on the preferences of other users with similar behavior. This approach is commonly represented using a User–Item Matrix, where rows represent users, co...
