As with the user-based approach, let's consider having two sets of elements: users and items. However, in this case, we don't assume that they have explicit features. Instead, we try to model a user-item matrix based on the preferences of each user (rows) for each item (columns). For example:
In this case, the ratings are bounded between 1 and 5 (0 means no rating), and our goal is to cluster the users according to their rating vector (which is, indeed, an internal representation based on a particular kind of feature). This allows producing recommendations even when there are no explicit ...
