How does Neural Collaborative filtering (NCF) handle new users (cold start)? I am building model with NCF and i know NCF has great power to handle sparse data, and find great representation for users and items. However, I don't know how would NCF handle new users? my structure is :
users_id -> embedding
items_id -> embedding
                  -> GMF + MLP -> concat() -> output

In this structure, if a new user_id appear in inference data, embedding layer may error out and I don't know whether NCF will apply "global mean" value to that new user_id.
In another word, how do you handle cold start users in NCF?

In the original paper, the author states the cold-start problem can be adjusted by replacing the user / item embedding with content features.

Note that with such a
generic feature representation for inputs, our method can be
easily adjusted to address the cold-start problem by using
content features to represent users and items.

To my understanding, the content features can be geographic information / profile / behavior of a new user or categorical feature for a new item. The model can still make an inference on a new user / item because content-based features for existing users and items are used during model training.
link to paper: https://arxiv.org/pdf/1708.05031v2.pdf
