Global Graph Attention for Contrastive Sequential Recommendation

Zhijin Chen, Dong Zhou, Jianghao Lin, Wentao Huang, Xingran Zhou, Aimin Yang

Abstract: Contrastive learning is a primary approach to mitigating data sparsity in sequential recommendation, but its improvements on the item side are limited due to data augmentation impairing user representations. To address these challenges, this paper introduces the Global Graph Attention for Contrastive Sequential Recommendation (GGACSR). GGACSR integrates graph neural networks and self-attention mechanisms, with an Attention Convolution Layer replacing nonlinear transformations in Graph Convolutional Networks (GCNs) with Q, K, and V vector operations, facilitating better handling of sequence dependencies. Leveraging a global user connection graph and projecting embeddings into a lower dimension effectively improves item and user representations. Overall, GGACSR outperforms existing baselines on three public datasets by more accurately capturing complex relationships and adapting user preferences.