With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.

翻译：随着社交媒体的普及，越来越多的用户在日常生活中搜索并参与群体活动。这催生了对群体识别（GI）任务的研究需求，即向用户推荐群体。该任务的主要挑战在于如何基于用户以往的群体参与行为以及用户对项目的兴趣，预测用户对群体的偏好。尽管图神经网络（GNNs）的最新进展能够在基于图的推荐系统中嵌入多种类型的对象，但这些方法未能全面解决群体识别问题。本文提出了一种名为"基于过渡超图卷积与图自监督学习的群体识别（GTGS）"的新框架。我们设计了一种新型的过渡超图卷积层，在探索用户群体偏好时，将用户对项目的偏好作为先验知识加以利用。为构建面向群体识别任务的全面用户/群体表示，我们设计了跨视图自监督学习以增强每个用户项目偏好与群体偏好的内在一致性，并引入基于群体的正则化以提升群体嵌入之间的区分度。在三个基准数据集上的实验结果验证了GTGS的优越性。此外，我们进行了详细的消融研究以证明所提框架的有效性。