where c is the confidence that the actual rating ya will lie in the interval between the
lower bound bl(Ya) and the upper bound bu(Ya). For example, it is possible for the
system to be certain that an item will be assigned a rating between 3 and 5 with a
probability c = 90%. Many methods prefer items with a higher upper bound, indicating that an item may be rated highly (good for exploitation), and if the confidence
interval is also wide then it may be good for exploration. In some cases where it is
desirable to increase the number of items predicted to be more highly rated, it may
be beneficial to use the expected change in the lower bound of the confidence interval for selecting an item [38], the higher the expected change the more desirable.