  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.
