I am trying to do collaborative filtering for implicit feedback datasets by following the seminal paper:
The section on ranking says:
I have a matrix of 50K X 9K with each cell having no. of times a user has seen a video. I also have a binary matrix of the same (watched or not; 1 or 0)
If I divide the dataset into 80:20 in training and test, and run the Recommender algorithm on 80% training, how do I evaluate the above mentioned ranking algo on the test in Python. I guess I am having a bit of trouble in understanding the algo's implementation.