Amazon.com Recommendations: Item-to-Item Collaborative Filtering

This paper introduces a method for generating recommendations based on item-to-item collaborative filtering. Instead of matching users to similar users, this approach focuses on finding items similar to those a user has liked. This provides scalable and high-quality recommendations, especially in large datasets where traditional user-based methods struggle due to computational costs. The algorithm builds a similarity matrix between items and then recommends items similar to those a user has already interacted with, proving effective in the context of Amazon.com's vast product catalog.