Building a recommendation engine depends on whether the engine searches for related items or users when trying to recommend a particular item.

In item-based analysis, the engine focuses on identifying items that are similar to a particular item, while in user-based analysis, users similar to the particular user are determined first. For example, users with the same profile information (age, gender, and so on) or action history (bought, watched, viewed, and so on) are determined, and then the same items are recommended to other, similar users.

Both approaches require us to compute a similarity matrix, depending on whether we're analyzing item attributes or user actions. Let's take a deeper look at how this is done.