You have mentioned an aspect of collaborative-based recommender systems that also made me feel like WHAT???
Yes: In a Collaborative-based recommender system, if you have a new user, you basically have to retrain your model to include that user in the next predictions. That is, if you want to include that user in the collaborative-based recommender system.
Every time you have a new user you face, with that user, the Cold Start problem, specially if you don’t have any information about that user.
Now, there are “work-arounds”. For example:
Example 1. You gather some information about the user on on-boarding, like Location, Age, Gender, and may be you directly ask the user for some of their preferences. In this case, you can leverage on the profiles created with your current users, and based on that produce recommendations to this new user, by using the current model. At some point, for example every night, or every week, you’ll want to re-train your model and include all your new users.
Example 2: For your new user(s), where you don’t have any information about them to begin with, your can start by using a Content-Based recommender system, which would be based on your items (products, movies, songs, or any other type of item). You start with recommendations from your content based system, and as the user rates (or sees, or listens to, or selects) the recommendations, you start learning about this new user’s preferences. At some point, you’ll have enough information about this new user to add them in the next retraining of your Collaborative-Based Recommender System.
Hope this sheds light on this topic!