  Package-to-group recommender systems recommend a set of unified items to a
group of people. Different from conventional settings, it is not easy to
measure the utility of group recommendations because it involves more than one
user. In particular, fairness is crucial in group recommendations. Even if some
members in a group are substantially satisfied with a recommendation, it is
undesirable if other members are ignored to increase the total utility. Many
methods for evaluating and applying the fairness of group recommendations have
been proposed in the literature. However, all these methods maximize the score
and output only one package. This is in contrast to conventional recommender
systems, which output several (e.g., top-$K$) candidates. This can be
problematic because a group can be dissatisfied with the recommended package
owing to some unobserved reasons, even if the score is high. To address this
issue, we propose a method to enumerate fair packages efficiently. Our method
furthermore supports filtering queries, such as top-$K$ and intersection, to
select favorite packages when the list is long. We confirm that our algorithm
scales to large datasets and can balance several aspects of the utility of the
packages.
