Recommendation engines require the following pieces of input in order to make recommendations:

• Item information, described with attributes
• A user profile, such as age range, gender, location, friends, and so on
• User interactions, in the form of rating, browsing, tagging, comparing, saving, and emailing
• The context where the items will be displayed; for example, the item's category and the item's geographical location

This input is then combined by the recommendation engine to help obtain the following:

• Users who bought, watched, viewed, or bookmarked this item also bought, watched, viewed, or bookmarked
• Items similar to this item
• Other users you may know
• Other users who are similar to you

Now, let's take a closer look at how this combination works.