Enumerating Fair Packages for Group Recommendations
Enumerating Fair Packages for Group Recommendations
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DOI：
10.1145/3488560.3498432
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
R. Sato
中科院分区：
文献类型：
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作者：
R. Sato
包到组推荐系统向一组人推荐一组统一的项目。与传统设置不同，衡量组推荐的效用并不容易，因为它涉及多个用户。在小组推荐中，公平性尤为重要。即使小组中的一些成员对建议非常满意，如果忽略其他成员以增加总效用，这也是不可取的。文献中提出了许多评估和应用小组推荐公平性的方法。但是，所有这些方法都将分数最大化，并且只输出一个包。这与传统的推荐系统形成对比，后者输出几个（例如，top-K）候选人。这可能是有问题的，因为一个群体可能由于一些未观察到的原因而对推荐的包不满意，即使分数很高。为了解决这一问题，我们提出了一种有效枚举公平包的方法。我们的方法还支持过滤查询，例如top-K和intersection，以便在列表较长时选择喜欢的包。我们确认我们的算法可以扩展到大型数据集，并且可以平衡包的几个方面的效用。
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.