Hi,
I was taking the coursera lecture and was more or less confused in the week2 recommender systems.
In the mean normalization explanation, when a new user has no ratings, it was said that it helps the machine learning to run more efficiently and I am not sure why this is the case.
The 5th new user has no ratings so far, and it can be masked with r=0 which has been done for other users with some number of ratings as far as I understood. Is it because of the regularization penalizing w and x and thus making the machine to say the 5th new user will give all 0 ratings for all movies which is not reasonable? – this makes sense, OK.
But is this strictly limited users with no ratings yet so far? Or is this mean normalization replacing the whole masking with r=0 that was explained previously?
And also why would this be more “efficient”? Is it in terms of speed?
Thanks!