Group recommendation aims to suggest desired items for a group of users. Existing methods can achieve inspiring results in predicting the group preferences in data-rich groups. However, they could be ineffective in supporting cold-start groups due to their sparsity interactions, which prevents the model from understanding their intent. Although cold-start groups can be alleviated by meta-learning, we cannot apply it by using the same initialization for all groups due to their varying preferences. To tackle this problem, this paper proposes a memory-augmented meta-optimized model for group recommendation, namely GroupMO. Specifically, we adopt a clustering method to assemble the groups with similar profiles into the same cluster and design a representative group profile memory to guide the preliminary initialization of group embedding network for each group by utilizing those clusters. Besides, we also design a group shared preference memory to guide the prediction network initialization at a more refined granularity level for different groups, so that the shared knowledge can be better transferred to groups with similar preferences. Moreover, we incorporate those two memories to optimize the meta-learning process. Finally, extensive experiments on two real-world datasets demonstrate the superiority of our model.

群组推荐旨在为一组用户推荐所需的商品。现有方法可以在预测数据丰富的群体中的群体偏好方面取得令人鼓舞的结果。然而，由于它们的交互稀疏，它们可能无法有效地支持冷启动组，从而阻止模型理解它们的意图。虽然冷启动组可以通过元学习来缓解，但由于它们的偏好不同，我们不能通过对所有组使用相同的初始化来应用它。为了解决这个问题，本文提出了一种用于群体推荐的记忆增强元优化模型，即GroupMO。具体来说，我们采用聚类方法将具有相似轮廓的组组装到同一簇中，并设计一个代表性组轮廓存储器，以利用这些簇指导每个组的组嵌入网络的初步初始化。此外，我们还设计了一个群体共享偏好记忆，以更细​​化的粒度级别指导不同群体的预测网络初始化，从而使共享知识能够更好地转移到具有相似偏好的群体。此外，我们结合这两个记忆来优化元学习过程。最后，对两个现实世界数据集的广泛实验证明了我们模型的优越性。