User specific recommendations
During the remainder of this chapter, we will focus on user-specific ratings. Let's start by considering a model of the recommendation system.
Let's assume:
C = Set of customers.
I = Set of items (could be movies, books, news items, and so on).
R = Set of ratings. This is an ordered set, where higher numbers indicate the high likeness of a particular item, whereas the lower value indicates a low likeness of a particular item. Generally this is represented by a real value between 0 and 1.
Let's define a utility function u, which looks at every pair of customers and items and maps it to a specific rating:
u: C * I → R
Let's give an example of a utility matrix, for a set of movies and users:
|
Godfather I |
Godfather II |
Good Will Hunting |
A Beautiful Mind | |
|
Roger |
1 |
0.5 | ||
|
Aznan |
1 |
0.7 |
0.2 | |
|
Fawad |
0.9 |
0.8 |
0.1 | |
|
Adrian |
1 |
0.8 |
A utility matrix is generally a sparse matrix, as users rate fewer movies than they watch. The areas where ratings are missing can be either...
