A key group decision making task is to aggregate individual preferences. Conventional group decision methods adopt pre-defined and fixed strategies to aggregate individuals' preferences, which can be ineffective due to the varying importance and influence of individual group members. Recent studies have proposed to assign different weights to individual members automatically based on the level of consistency of their ratings with group assessment outcomes. However, they ignored the high-order influence relationship among individual group members on group decision making. In this study, from a group recommendation perspective, we propose a novel collaborative Group Embedding and Decision Aggregation (GEDA) approach by leveraging the graph neural network technique to address those limitations. Specifically, GEDA first deploys a graph convolution operation on user-item interaction and group-item interaction graphs to generate embedding representations of members, groups, and items. A novel multi-attention (MA) module then learns each member's decision weight by simultaneously considering the relationships among members for aggregating individual preferences into group preferences. The empirical evaluation using two real-world datasets demonstrates the advantage of the proposed GEDA model over the state-of-the-art group recommendation models.

一项关键的群体决策任务是汇总个人偏好。传统的群体决策方法采用预先定义和固定的策略来聚合个人的偏好，由于个体群体成员的重要性和影响力不同，这种方法可能是无效的。最近的研究提出，根据个人评分与小组评估结果的一致性程度，自动为个人成员分配不同的权重。然而，他们忽略了个体群体成员之间对群体决策的高阶影响关系。在这项研究中，我们从群体推荐的角度提出了一种新的协作群体嵌入和决策聚合 (GEDA) 方法，利用图神经网络技术来解决这些局限性。具体来说，GEDA 首先在用户-项目交互图和组-项目交互图上部署图卷积运算，以生成成员、组和项目的嵌入表示。然后，一个新颖的多注意力 (MA) 模块通过同时考虑成员之间的关系来学习每个成员的决策权重，以将个人偏好聚合到群体偏好中。使用两个真实世界数据集的实证评估证明了所提出的 GEDA 模型优于最先进的群组推荐模型的优势。通过同时考虑成员之间的关系来将个人偏好聚合为群体偏好来增加决策权重。使用两个真实世界数据集的实证评估证明了所提出的 GEDA 模型优于最先进的群组推荐模型的优势。通过同时考虑成员之间的关系来将个人偏好聚合为群体偏好来增加决策权重。使用两个真实世界数据集的实证评估证明了所提出的 GEDA 模型优于最先进的群组推荐模型的优势。