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 ...
