At 1:17:00 If your values are continuous you can keep a threshold for eg: thres = 2 5 - 4 < thres is considered close whereas 5 - 2 > thres it's not close. Now, you have a binary matrix for precision and recall

17:00 Calculating the average rating

Dear Jil, Your tutorial is extremely helpful and well presented - well done! However, I do have a small question about the section where You discuss sparsity. In most documentation I see that sparsity is measured by getting the quotient of number of zero or missing values / total number of elements in the matrix. However, You're taking the number of ratings (rather than missing values) into the formula - why? Considering that we have 610x9724=5,931,640 elements and 100,836 ratings - it would seem that our sparsity should be quite big (as most data is missing)! Also, could You point to any documentation which discussed at which levels of sparsity the CF methods should be used? Best regards, Pawel.

matrix sparsity of MovieLens 20M Dataset using your code is 0.54% where my findings imply the matrix sparsity is 100-0.54=99.46% which makes more sense after all. I dont understand. isnt the formula sparsity=(#zero valued elements)/(#total elements) ?