Pairwise preference information, which involves users expressing their preferences by comparing items, plays a crucial role in decision-making and has recently found application in recommendation systems. In this study, we introduce GcPp, a clustering algorithm that leverages pairwise preference data to generate recommendations for user groups. Initially, we construct individual graphs for each user based on their pairwise preferences and utilize a graph convolutional network to predict similarities between all pairs of graphs. These predicted similarity scores form the foundation of our research. We then construct a new graph where users are nodes and the edges are weighted according to the predicted similarities. Finally, we perform clustering on the graph’s nodes (users). By evaluating various metrics, we found that employing a similarity metric based on a convolutional neural network (SimGNN) with our proposed ground truth called Top-K yielded the highest accuracy. The proposed approach is specifically designed for group recommendation systems and holds significant potential for group decision-making problems. Code is available at .

中文翻译：

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基于成对偏好的群体推荐系统图神经方法

成对偏好信息涉及用户通过比较项目来表达他们的偏好，在决策中发挥着至关重要的作用，并且最近在推荐系统中得到了应用。在本研究中，我们引入了 GcPp，一种利用成对偏好数据为用户组生成推荐的聚类算法。最初，我们根据每个用户的成对偏好构建单独的图，并利用图卷积网络来预测所有图对之间的相似性。这些预测的相似性分数构成了我们研究的基础。然后，我们构建一个新图，其中用户是节点，并且根据预测的相似性对边进行加权。最后，我们对图的节点（用户）执行聚类。通过评估各种指标，我们发现采用基于卷积神经网络 (SimGNN) 的相似性指标以及我们提出的称为 Top-K 的地面实况可产生最高的准确度。所提出的方法是专门为群体推荐系统设计的，对于群体决策问题具有巨大的潜力。代码可在 处获取。