User 5273 | 6/8/2016, 6:11:09 PM
hi, All the recommenders world is new for me so please bare with me and help me with my question. In the classic machine learning algorithms we have examples X with Y the tags of the examples. In order to evaluate a prediction (hypothesize) function f we compare Y[i] with f(X[i]). However, in recommendation algorithm we don't have Y tags and only fill missing ratings per items and users. What is compared in the test examples that let us evaluate the algorithm? and how does it done?
P.S.-I have googled the question and read the docs that I found on the provided evaluating functions (evaluate.rmse, evaluateprecisionrecall).
I really appreciate any help in this matter. Thanks!