Leave-one-out cross validation is probably the most straight-forward way to address this. If you happen to be using a model that requires a lot of time to train, then leave n% out might be more appropriate.
The method involves eliminating one known rating and trying to predict it. If you want to remove n percent of the ratings, just choose them randomly rather than choosing a specific number of every user's ratings. And keep n pretty small - on the order of 10% or less.
Here is a good treatment of cross validation methods for recommender systems. Generally:
Holdout is a method that splits a dataset into two parts: a training
set and a test set. These sets could have different proportions. In
the setting of recommender systems the partitioning is performed by
randomly selecting some ratings from all (or some of) the users. The
selected ratings constitute the test set, while the remaining ones are
the training set. This method is also called leave-k-out. In ,
Sarwar et al. split the dataset into 80% training and 20% test data.
In  several ratios among training and test (from 0.2 to 0.95 with
an increment of
0.05) are chosen and for each one the experiment is repeated ten times with different training and test sets and finally the results are
averaged. In  the test set is made by 10% of users: 5 ratings for
each user in the test set are withheld.
Leave-one-out is a method
obtained by setting k = 1 in the leave-k-out method. Given an active
user, we withhold in turn one rated item. The learning algorithm is
trained on the remaining data. The withheld element is used to
evaluate the correctness of the prediction and the results of all
evaluations are averaged in order to compute the final quality
estimate. This method has some disadvantages, such as the overfitting
and the high computational complexity. This technique is suitable to
evaluate the recommending quality of the model for users who are
already registered as members of the system. Karypis et al. 
adopted a trivial version of the leave-one-out creating the test set
by randomly selecting one of the non-zero entries for each user and
the remaining entries for training. In , Breese et al. split the
URM in training and test set and then, in the test set, withhold a
single randomly selected rating for each user.
A simple variant of the
holdout method is the m-fold cross-validation. It consists in dividing
the dataset into m independent folds (so that folds do not overlap).
In turn, each fold is used exactly once as test set and the remaining
folds are used for training the model. According to  and , the
suggested number of folds is 10. This technique is suitable to
evaluate the recommending capability of the model when new users
(i.e., users do not already belong to the model) join the system. By
choosing a reasonable number of folds we can compute mean, variance
and confidence interval.
Hope this helps!