AlignGroup：学习并对齐群体共识与成员偏好以实现群体推荐
AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
AI总结：
AlignGroup 提出通过超图神经网络学习群体共识，并利用自监督对齐任务将群体共识与成员共同偏好对齐，从而同时兼顾群体层面一致性和个体细粒度偏好，在群体推荐和用户推荐任务上均取得最优效果。
摘要：
群体活动是人类社会中的重要行为，为群体提供个性化推荐被称为群体推荐任务。现有方法通常可分为两类推断群体偏好的策略：1）通过聚合成员的个性化偏好来确定群体偏好，2）通过捕捉群体成员在共同妥协后的一致决策来推断群体共识。然而，前者会缺乏群体层面的考虑，后者则忽视了个体用户的细粒度偏好。为此，我们提出了一种新颖的群体推荐方法 AlignGroup，它同时关注群体共识和群体成员的个体偏好，以推断群体决策。具体而言，AlignGroup 通过精心设计的超图神经网络探索群体共识，该网络能够高效地学习群体内部和群体之间的关系。此外，AlignGroup 创新性地利用自监督对齐任务，通过将群体共识与成员的共同偏好对齐来捕捉细粒度的群体决策。在两个真实世界数据集上的大量实验验证了我们的 AlignGroup 在群体推荐任务和用户推荐任务上均优于最先进的方法，并且在效率上也优于大多数基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
