Using a recommender system with new user

Question

I am using "implicit" package ( https://github.com/benfred/implicit ) to create a recommender system in python. More preciseling, I am using the implicit least square algorithm. The library is pretty easy to use, I was able to make predictions for already existing users, or to find similar items, no prob. But how can I make predictions for a new user which was not in input data? My goal is to get prediction from a new vector of items (~a new user). All items exist in input data. This library and other equivalent ones usually provide a predict method for user already existing in dataset. My first attempt was to get a prediction vector for each item and sum them all. But it does not feel right, does it? This seems like a common usage, so I think I am missing something. What would be the method to use? Thank you for your help.

Answer 1

depends on what you're recommending but for example if it is something like movies then to a new user we would just generally recommend the most popular movies . Then as we get to know more about the user we can use the usual matrix factorization.