I have a dataset that looks like this: Image taken from this blog
Let's assume that I have applied Matrix factorization and have learned the zero values for the items missing for every user. I now know in a Collaborative filtering manner the items a user likes the most.
My question is what then.
How would I use this information to recommend products to a user? Do I just take the products with the highest rating? I have found a lot of literature on non negative matrix factorization but I have not found a paper on how to actually recommend products given the factorized matrix?
Is this something you naively implement(highest rated products) and just measure performance both in the offline(using RMSE, NDCG) and online(using CTR) part of the RS?