A common approach is to iteratively present sets of alternative recommendations
to the user, and by eliciting feedback, guide the user towards an end goal in which
the scope of interest is reduced to a single item. This cycle-based approach can
be beneficial since users rarely know all their preferences at the start (becoming
self-aware), but tend to form and refine them during the decision making process
(exploration). Thus Conversation-based AL should also allow users to refine their
preferences in a style suitable to the given task. Such systems, unlike general RSs,
also include AL by design, since a user’s preferences are learned through active
interaction. They are often evaluated by the predictive accuracy, and also by the
length of interaction before arriving at the desired goal.