The purpose of group recommendation is to recommend items that all users in a group may like; therefore, the modeling target of group recommendation is not individual preference features, but group preference features, which are very complex in the context of online social platforms. In this paper, in order to better model group preferences, We propose the model Knowledge graph-based Multi-view Attention Group Recommendation (KMAGR) to model the group preference relationship in three aspects: 1) adding knowledge mapping relationships as side information to the model; 2) using attention mechanisms and graph neural network structures to model group purchase intentions; 3) in addition to modeling group preferences from the user’s perspective, we use group-user and group-intent multiple perspectives to model group preferences. We conducted experiments on two real online social datasets, and the experimental results proved that KMAGR outperformed other state-of-the-art models in group recommendation. Adding knowledge graph information and identifying group intent to the group recommendation system can greatly improve the effectiveness of group recommendation, while the critical path aggregation mechanism improves the explainability of recommendation results.

群组推荐的目的是推荐群组内所有用户都可能喜欢的物品；因此，群体推荐的建模目标不是个人偏好特征，而是群体偏好特征，这在在线社交平台的背景下非常复杂。在本文中，为了更好地建模群体偏好，我们提出了基于知识图谱的多视图注意群体推荐（KMAGR）模型，从三个方面对群体偏好关系进行建模：1）将知识映射关系作为辅助信息添加到模型中。模型; 2）利用注意力机制和图神经网络结构对团购意图进行建模；3）除了从用户的角度对群体偏好进行建模之外，我们还使用群体用户和群体意图多个视角来对群体偏好进行建模。我们在两个真实的在线社交数据集上进行了实验，实验结果证明 KMAGR 在群体推荐方面优于其他最先进的模型。在群体推荐系统中加入知识图谱信息和识别群体意图可以极大地提高群体推荐的有效性，而关键路径聚合机制则提高了推荐结果的可解释性。