Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute:

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

I am trying to evaluate my recommender system which uses Non-negative Matrix Factorization. Some things that I evaluate are

  • How does the size of the feature matrix affect the recommendations
  • How does the amount of "rated movies" affect the recommendation
  • ...

Should I evaluate these things with a usual trainingset-testset separation? Like 60% Trainingset and 40% Testset? What is about the fact that every factorization of NNMF leads a bit different results, because of non-convexity and random initialization? I would use a random 60/40 separation, but I thought I´d better ask before I do it wrong.

And if I would decide to make several runs of NNMF in order to select the best results, then I would need to do cross validation right? Or can I use the same training/testset for every run?

share|improve this question

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.