A IRecommenderEvaluator which computes the average absolute difference between predicted and actual ratings for users.

For each user, these implementation determine the top preferences, then evaluate the IR statistics based on a IDataModel that does not have these values. This number is the "at" value, as in "precision at 5". For example, this would mean precision evaluated by removing the top 5 preferences for a user and then finding the percentage of those 5 items included in the top 5 recommendations for that user.

Picks relevant items to be those with the strongest preference, and includes the other users' preferences in full.

Simple helper class for running load on a Recommender.

Evaluate recommender by comparing order of all raw prefs with order in recommender's output for that user. Can also compare data models.

A IRecommenderEvaluator which computes the "root mean squared" difference between predicted and actual ratings for users. This is the square root of the average of this difference, squared.