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.
翻译:在软件包建议中,一组项目被视为针对单一共同目标的统一一揽子方案,而常规建议系统则独立处理项目。例如,对于音乐播放列表建议,每个软件包(即播放列表)应当与类别相一致。在群集建议中,向一组用户推荐项目,而传统建议系统则向单个用户推荐项目。不同于常规设置,很难衡量集团建议的效用,因为它涉及不止一个用户。特别是,集团建议中的公平性至关重要。即使一个集团的一些成员对建议非常满意,但如果其他成员忽略了整体效用,则不宜增加整体效用。文献中已经提出了每个软件包(即播放列表)与群体建议之间的公平性。然而,所有这些软件都是向一组用户推荐项目,而传统建议系统则向单个用户推荐项目。这与常规推荐系统不同,其中输出数个(例如,顶值-K美元)候选人,这可能会有问题。由于某些未观察的原因,一个组的软件包可能与推荐的软件包不满意,即使评分高,因此不宜增加总效用。在文献中,每个软件组的软件包都提出各种评价方法的公平性标准,因此,我们的用户可以提出一个不同的搜索方法。我们采用的计算方法可能提出一个不同的搜索方法,因此,因此,我们的用户可以提出一个不同的搜索方法可以要求。我们采用的系统会要求不同的搜索方法。我们采用的系统采用一个不同的搜索方法。