I would like to know if there are any evaluation frameworks of recommender systems which are capable of evaluating rating prediction and topN recommendation (Precision and recall etc.). Maybe I need to find them in recommender frameworks? If so are they easy to plug in?
As a matter of fact I am working on dynamic recommender systems which may need the items for training are earlier than item for testing/recommendations, which is also the real application scenario I think.
In my point of view, as long as the data records have timestamps, the evaluation work should use training and testing items with time order. But many of the two sets of items are randomly selected. Am I right?