In general, a recommender computes a function rec(i, u) for a given item (i) and an user (u) that returns a recommendation score.
It expresses the system’s belief as to whether the item is of interest for the user.
In case the RS’s knowledge base consists solely of hard constraints,
the assumed utility scores either 1 if an item satisfies all applicable constraints or 0 otherwise.