Hi, If one is using a matrix factorization based method, in order to generate a top-N recommendation to a user, all the unknown ratings of that user needs to be predicted (so that highest predicted N items can be recommended). If we are talking about a site with millions of items this means that to make a top-N recommendation to a user, that user's rating on millions of items need to be predicted. This seems rather an inefficient way. I have two questions:
First one is general: do you know how this is can be done in a more efficient way, or how real large sites do this. Second, how can I do this efficiently with Mahout. Thanks mario
