In a hybrid recommendation system, there are two classes of entities: users and items (examples are movies, products, and so on). Now, as a user, you might have preferences for certain items. Therefore, these preferences must be extracted from data about items, users, or ratings. Often this data is represented as a utility matrix, such as a user-item pair. This type of value can represent what is known about the degree of preference of that user for a particular item. The entry in the matrix, that is, a table, can come from an ordered set. For example, integers 1-5 can be used to represent the number of stars that the user gave as a rating for items.
We have argued that often users might not have rated items; that is, most ...
