The data used in a recommendation system is divided in two categories: the users and the items. Each user likes certain items, and the rating value r_ij (from 1 to 5) is the data associated with each user i and item j and represents how much the user appreciates the item. These rating values are collected in matrix, called utility matrix R, in which each row i represents the list of rated items for user i while each column j lists all the users who have rated item j. In our case, the data folder ml-100k contains a file called u.data (and also u.item with the list of movie titles) that has been converted into a Pandas DataFrame (and saved into a csv, utilitymatrix.csv) by the following script:

The output of the first two lines is as follows:

Each column name, apart from the first (which is the user id), defines the name of the movie and the ID of the movie in the MovieLens database (separated by a semicolon). The 0 values represent the missing values and we expect to have a large...