23.5 Uncertainty-based Active Learning
Uncertainty-based AL tries to obtain training points so as to reduce uncertainty in
some aspect, such as concerning output values [28], the model’s parameters [23], a
decision boundary [45], etc. A possible drawback to this approach is that reducing
uncertainty may not always be effective. If a system becomes certain about user
ratings, it does not necessarily mean that it will be accurate, since it could simply be
certain about the wrong thing (i.e., if the algorithm is wrong, reducing uncertainty
will not help). As an example, if the user has so far rated items positively, a system
may mistakenly be certain that a user likes all of the items, which is likely incorrect