I'm working on something and I have a question... • /r/MachineLearning

I'm working in a content based recommendation system, where the recommended content to the users is not based on what he liked, but based on what his doctor evaluated. So, the doctor check some things for a patient and evaluate positively or negatively and the patient gets recommendations of similar things, besides what his doctor thought it was important for him. The question is: seeing a lot of articles in recommendation systems that uses content based filtering, I notice those use the same learning algorithms to create and update the user's profile, but I don't understand in what part of the process this algorithm should be used. For example, I understand that the steps to create the recommendation system are these: -create a user profile (based in personal and explicit information and in his behaviour related to the content [number of times the doctor visualizes some item, or evaluation he gives to the item]); -create the profile that's gonna be recommended (and then compare the similarity with other items); -create something that counts the current user profile and recommend itens that are similar to that profile. So will I use the learning algorithm in this?