Enumerating Fair Packages for Group Recommendations
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In package recommendations, a set of items is regarded as a unified package towards a single common goal, whereas conventional recommender systems treat items independently. For example, for music playlist recommendations, each package (i.e., playlist) should be consistent with respect to the genres. In group recommendations, items are recommended to a group of users, whereas conventional recommender systems recommend items to an individual user. Different from the conventional settings, it is difficult 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. Various 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 a single 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. In particular, each fairness measure is not absolute, and users may call for different fairness criteria than the one adopted in the recommender system in operation. To address this issue, we propose a method to enumerate fair packages so that a group can select their favorite packages from the list. Our proposed method can enumerate fair packages efficiently, and users can search their favorite packages by various filtering queries. We confirm that our algorithm scales to large datasets and can balance several aspects of the utility of the packages.
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