Amazon has created perhaps the most highly functioning and relevant recommendation system developed in-house, called item-to-item collaborative filtering.
"Rather than matching the user to similar customers, item-to-item collaborative filtering matches each of the user's purchased and rated items to similar items, then combines those similar items into a recommendation list," according to an industry report describing Amazon's methods.
The system revolves around a table of similar items based on what items Amazon users are purchasing at the same time. Each item a user purchases is looked at individually and recommendations are generated from comparing each one to the similar-items table.
Item-to-item collaborative filtering generates extremely user-relevant content, and can do so even with little user input because of how it uses similar items instead of similar users.