Enumerating Fair Packages for Group Recommendations

TL;DR

This paper introduces an efficient method to enumerate multiple fair package recommendations for groups, addressing fairness and utility balance, and supporting filtering queries for better selection.

Contribution

It proposes a novel algorithm that enumerates multiple fair packages for group recommendations, unlike existing methods that output only one, enhancing fairness and utility.

Findings

Algorithm scales to large datasets

Balances multiple utility aspects

Supports filtering queries like top-K and intersection

Abstract

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.