Getting recommendations for a new user

In the example of producing movie recommendations for a set of users, given that we are interested in making online-predictions, I would assume that re-training of the model would take place maybe once per day or once per week.

Pose that a new, previously unknown user, watches a movie Friday night before we re-train the model the next day. How would we go about to recommend movies to this user late Friday night when he/she might want to watch another one? The user is currently not in our model, but we know which movie he/she watched. One solution would be to query the model for the user, and return a out-of-vocabulary recommendations for the user, although, these recommendations would not take into consideration the movie the user just watched.

Is there a way to find similar users to the one in question, and base the recommendations on these similar users somehow? I could see how we could get the user embeddings for all users, as well as the movie embeddings for all movies, and dot-multiply those matrices to get a user/movie matrix, which we then could somehow (unknown to me) get similar users.

Any ideas how to use this kind of recommendation model when dealing with unknown users at serving-time?