For each item, the parameters are trained with all ratings
relevant to it, i.e all training users who have rated that item.
The average In the datasets that will be used in the experiments,
each item has enough ratings among training users that
Therefore, items have trained enough in the training phase and
updating corresponding item parameters after getting a new
rating from the test user does not effect them too much.