Amazon Recommendation

Industry Report

Amazon.com Recommendations

Item-to-Item Collaborative Filtering

Greg Linden, Brent Smith, and Jeremy York • Amazon.com ecommendation algorithms are best known for their use on e-commerce Web sites,1 where they use input about a customer’s interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. The click-through and conversion rates — two important measures of Web-based and email advertising effectiveness — vastly exceed those of untargeted content such as banner advertisements and top-seller lists. E-commerce recommendation algorithms often operate in a challenging environment. For example:

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There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm’s online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in realtime, scales to massive data sets, and generates highquality recommendations.

Recommendation Algorithms

Most recommendation algorithms start by finding a set of customers whose purchased and rated items overlap the user’s purchased and rated items.2 The algorithm aggregates items from these similar customers, eliminates items the user has already purchased or rated, and recommends the remaining items to the user. Two popular...