Item-item collaborative filtering, or item-based, or item-to-item, is a form of collaborative filtering for recommender systems based on the similarity between items calculated using people's ratings of those items.
[3] Earlier collaborative filtering systems based on rating similarity between users (known as user-user collaborative filtering) had several problems: Item-item models resolve these problems in systems that have more users than items.
Item-item models use rating distributions per item, not per user.
First, the system executes a model-building stage by finding the similarity between all pairs of items.
Consider the following matrix : If a user is interested in Article 1, which other item will be suggested to him by a system which is using Amazon's item-to-item algorithm ?