Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences: 1) determining group preferences by aggregating members' personalized preferences, and 2) inferring group consensus by capturing group members' coherent decisions after common compromises. However, the former would suffer from the lack of group-level considerations, and the latter overlooks the fine-grained preferences of individual users. To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. Specifically, AlignGroup explores group consensus through a well-designed hypergraph neural network that efficiently learns intra- and inter-group relationships. Moreover, AlignGroup innovatively utilizes a self-supervised alignment task to capture fine-grained group decision-making by aligning the group consensus with members' common preferences. Extensive experiments on two real-world datasets validate that our AlignGroup outperforms the state-of-the-art on both the group recommendation task and the user recommendation task, as well as outperforms the efficiency of most baselines.

翻译：群组活动是人类社会中的重要行为，为群组提供个性化推荐的任务被称为群组推荐。现有方法通常可分为两种策略来推断群组偏好：1）通过聚合成员的个性化偏好来确定群组偏好；2）通过捕捉群组成员在共同妥协后的一致决策来推断群组共识。然而，前者缺乏群组层面的考量，后者则忽视了单个用户的细粒度偏好。为此，我们提出了一种新颖的群组推荐方法 AlignGroup，该方法同时关注群组共识和群组成员的个体偏好，以推断群组决策。具体而言，AlignGroup 通过精心设计的超图神经网络有效学习群组内和群组间的关系，从而探索群组共识。此外，AlignGroup 创新性地利用自监督对齐任务，通过将群组共识与成员的共同偏好对齐，来捕捉细粒度的群组决策。在两个真实数据集上的大量实验验证了我们的 AlignGroup 在群组推荐任务和用户推荐任务上均优于现有最先进方法，并且在效率上也超越了大多数基线模型。