MA-GCL4SR: Improving Graph Contrastive Learning-Based Sequential Recommendation with Model Augmentation

Abstract

Sequential recommendation (SR) has leveraged the advantages of graph contrastive learning (GCL) to enhance the representation of SR, which mitigates to some extent the constraint of scarce labeled data for supervision in SR. Existing work applies general graph data augmentation strategies to generate positive sample pairs, then further representation learning is conducted through a shared graph neural network. In this study, we identify limitations in applying traditional GCL to sequential recommendation: after the data augmentation, the shared graph neural network architecture used for feature learning fails to supply sufficiently diverse contrastive views, which are necessary to effectively identify and focus on the key information that is truly relevant for sequential recommendation. To ease this limitation, we propose a novel framework named Model Augmented Graph Contrastive Learning for Sequential Recommendation (MA-GCL4SR), which emphasizes modifying the internal architectures of the graph neural network through the use of model augmentation strategies, rather than focusing on making improvements during the data augmentation phase before encoding. Thereby, we construct a non-shared view encoder for SR, enriching the samples of user’s interaction sequences and strengthen the stability of the augmented sequence. Extensive experiments on four real-world datasets confirm the effectiveness of the proposed MA-GCL4SR paradigm, showcasing its consistent ability to elevate model performance across various real-world scenarios.