How do I perform Leave One Out Cross Validation For Top n Recommendation Sytems?
Here is my procedure for computation of the "Hit Rate with leave-one-out cross validation":


leave only one actual user-item interaction (this can be rating, purchase or whatever you are dealing with in your data) out of the train dataset (this gives you a total of U "unseen" interactions provided that you have at least one interaction per user);
Generate top n recommendations for all user with whatever recommendation system you are using;
if a user actually interacted with the recommended item (is the left-out" item was among the top n recommendations for that particular user) => that's a hit
Hit rate = sum of all the hits across the users (this can be at most U since you can have at most 1 hit per user) DIVIDED BY the number of users (U).