GC–HGNN: A global-context supported hypergraph neural network for enhancing session-based recommendation

Highlights：

• In order to fully consider the global- and local-level context information, all session sequences are modeled as a hypergraph and the current session sequence is modeled as local session graph. In particular, each node in the local session graph constructed in this paper is connected to an implicit dynamic mediation node.

• Our proposed model uses hypergraph convolutional neural network to capture complex higher-order relationships between items and graph attention network to learn the pairwise item transiting relationships.

• In our work, sum-pooling operation is adopted to fuse the global and local-level context information, and reversed position information is incorporated. Specifically, the proposed model uses the attention mechanism to get the final session representation.

• The results of extensive experiments demonstrate that our proposed model consistently outperforms the state-of-the-art methods.