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-enhanced sequential Recommender system (MAERec) 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 https://github.com/HKUDS/MAERec.

翻译：尽管一些强大的神经网络架构（例如Transformer、图神经网络）通过高阶项目依赖建模在序列推荐中取得了更优的性能，但在标签稀缺场景下，它们可能存在表征能力不足的问题。为解决标签不充分的问题，对比学习在近期方法中备受关注，它通过嵌入对比进行数据增强以实现自监督学习。然而，由于对比视图生成策略依赖人工设计，现有对比学习增强模型（i）难以在多样化的序列推荐任务中保持一致的性能；（ii）可能无法抵御用户行为数据噪声。鉴于此，我们提出了一种简单而有效的图掩码自编码器增强的序列推荐系统（MAERec），该系统能够自适应且动态地提取全局项目转移信息用于自监督增强。该方法自然避免了上述对构建高质量嵌入对比视图的严重依赖问题，而是设计了一种自适应数据重构范式，将其与长程项目依赖建模相结合，为序列推荐提供信息性增强。大量实验证明，我们的方法显著优于最先进的基线模型，并且能够在数据噪声与稀疏性条件下学习到更准确的表征。我们实现的模型代码已公开于 https://github.com/HKUDS/MAERec。