Session-based recommendation (SBR) aims to predict the next item at a certain time point based on anonymous user behavior sequences. Existing methods typically model session representation based on simple item transition information. However, since session-based data consists of limited users' short-term interactions, modeling session representation by capturing fixed item transition information from a single dimension suffers from data sparsity. In this paper, we propose a novel contrastive multi-level graph neural networks (CM-GNN) to better exploit complex and high-order item transition information. Specifically, CM-GNN applies local-level graph convolutional network (L-GCN) and global-level network (G-GCN) on the current session and all the sessions respectively, to effectively capture pairwise relations over all the sessions by aggregation strategy. Meanwhile, CM-GNN applies hyper-level graph convolutional network (H-GCN) to capture high-order information among all the item transitions. CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN. CM-GNN averages the item representations obtained by H-GCN to obtain high-order relation-based session representation. Moreover, to convert the high-order item transition information into the pairwise relation-based session representation, CM-GNN maximizes the mutual information between the representations derived from the fusion module and the average pool layer by contrastive learning paradigm. We conduct extensive experiments on multiple widely used benchmark datasets to validate the efficacy of the proposed method. The encouraging results demonstrate that our proposed method outperforms the state-of-the-art SBR techniques.

翻译：会话推荐（SBR）旨在基于匿名用户行为序列预测某个时间点的下一项。现有方法通常基于简单的项目转换信息建模会话表示。然而，由于会话数据仅包含用户有限的短期交互，从单一维度捕获固定项目转换信息来建模会话表示会面临数据稀疏性问题。本文提出一种新颖的对比多层级图神经网络（CM-GNN），以更好地利用复杂且高阶的项目转换信息。具体而言，CM-GNN 对当前会话应用局部层级图卷积网络（L-GCN），并对所有会话应用全局层级网络（G-GCN），通过聚合策略有效捕获所有会话中的成对关系。同时，CM-GNN 引入超层级图卷积网络（H-GCN）以捕获所有项目转换之间的高阶信息。CM-GNN 进一步设计基于注意力的融合模块，通过融合 L-GCN 和 G-GCN 生成的项目表示，学习基于成对关系的会话表示。CM-GNN 对 H-GCN 得到的项目表示取平均，获得基于高阶关系的会话表示。此外，为将高阶项目转换信息转化为基于成对关系的会话表示，CM-GNN 通过对比学习范式最大化融合模块与平均池化层所得表示之间的互信息。我们在多个广泛使用的基准数据集上开展了大量实验以验证所提方法的有效性。令人鼓舞的结果表明，我们的方法优于当前最先进的 SBR 技术。