In lesson 4, there was an introduction to collaborative filtering. The excel table at 1:43:20 (https://youtu.be/V2h3IOBDvrA?t=1h43m20s) explained the math behind it very good.

However, I do not understand how to make a prediction for a new user. In the process of training, each user gets assigned 5 factors. But if I now have a new user, how do I find out those 5 factors for him?

I have the same problem with the keras implementation: https://github.com/fastai/courses/blob/master/deeplearning1/nbs/lesson4.ipynb

We can use the model to generate predictions by passing a pair of ints - a user id and a movie id. For instance, this predicts that user #3 would really enjoy movie #6.

`model.predict([np.array([3]), np.array([6])])`

I do not understand how it can be useful to ask the network how much a user, which was already existent when training the network, likes a particular movie. I would rather like to ask the network â€śGiven movies w, x, y with rating a, b, c, how much would the user like movie z?â€ť