The science of producing predictions about what content people want to see next is based on lots of data. For example, things that are particularly interesting to a recommendation engine are items that have been seen in conjunction with other items, things downloaded in the same session, and content browsed before and after a purchase, to name a few. These relationships are known to the engine as interesting, because algorithms have been designed to look for them.