While some powerful neural network architectures (e.g., Transformer, Graph
Neural Networks) have achieved improved performance in sequential
recommendation with high-order item dependency modeling, they may suffer from
poor representation capability in label scarcity scenarios. To address the
issue of insufficient labels, Contrastive Learning (CL) has attracted much
attention in recent methods to perform data augmentation through embedding
contrasting for self-supervision. However, due to the hand-crafted property of
their contrastive view generation strategies, existing CL-enhanced models i)
can hardly yield consistent performance on diverse sequential recommendation
tasks; ii) may not be immune to user behavior data noise. In light of this, we
propose a simple yet effective graph masked autoencoder that adaptively and
dynamically distills global item transitional information for self-supervised
augmentation. It naturally avoids the above issue of heavy reliance on
constructing high-quality embedding contrastive views. Instead, an adaptive
data reconstruction paradigm is designed to be integrated with the long-range
item dependency modeling, for informative augmentation in sequential
recommendation. Extensive experiments demonstrate that our method significantly
outperforms state-of-the-art baseline models and can learn more accurate
representations against data noise and sparsity. Our implemented model code is
available at this https URL

提出了一种简单而有效的图蒙版自编码器，通过自监督学习来自适应动态提取全局项转换信息，在顺序推荐中进行信息增强。