Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each session consists of a series of items, and each user owns a series of sessions. However, the existing PSBR methods can merely capture the pairwise relation information within items and users. To effectively capture the hierarchical information, we propose a novel hierarchical hypergraph neural network to model the hierarchical architecture. Moreover, considering that the items in sessions are sequentially ordered, while the hypergraph can only model the set relation, we propose a directed graph aggregator (DGA) to aggregate the sequential information from the directed global item graph. By attentively combining the embeddings of the above two modules, we propose a framework dubbed H3GNN (Hybrid Hierarchical HyperGraph Neural Network). Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed model compared to the state-of-the-art methods, and ablation experiment results validate the effectiveness of all the proposed components.

基于会话的个性化推荐（PSBR）是现实世界中一项普遍且具有挑战性的任务，旨在根据会话的项目转换信息和相应用户的历史会话来推荐会话的下一个点击项目。会话被定义为短时间内交互的一系列项目。PSBR问题具有自然的层次结构，其中每个会话由一系列项目组成，并且每个用户拥有一系列会话。然而，现有的 PSBR 方法只能捕获项目和用户内的成对关系信息。为了有效地捕获层次信息，我们提出了一种新颖的层次超图神经网络来建模层次结构。此外，考虑到会话中的项目是顺序排序的，而超图只能建模集合关系，我们提出了一种有向图聚合器（DGA）来聚合来自有向全局项目图的顺序信息。通过仔细结合上述两个模块的嵌入，我们提出了一个名为 H3GNN（混合分层超图神经网络）的框架。对三个基准数据集的广泛实验证明了我们提出的模型与最先进的方法相比的优越性，并且消融实验结果验证了所有提出的组件的有效性。