Using the recommendation model
Now that we have our trained model, we're ready to use it to make predictions. These predictions typically take one of two forms: recommendations for a given user and related or similar items for a given item.
In this case, we would like to generate recommended items for a given user. This usually takes the form of a top-K list, that is, the K items that our model predicts will have the highest probability of the user liking them. This is done by computing the predicted score for each item and ranking the list based on this score.
The exact method to perform this computation depends on the model involved. For example, in user-based approaches, the ratings of similar users on items are used to compute ...