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 https://github.com/HKUDS/GMRec.

翻译：尽管一些强大的神经网络架构（例如Transformer、图神经网络）通过高阶物品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面临表示能力不足的问题。为解决标签不足的问题，对比学习近年来受到广泛关注，其通过嵌入对比进行数据增强以实现自监督学习。然而，由于这些方法手工设计的对比视图生成策略，现有对比学习增强模型：i) 难以在不同序列推荐任务上保持一致的性能；ii) 可能无法抵御用户行为数据噪声。鉴于此，我们提出一种简单而有效的图掩码自编码器，能够自适应且动态地蒸馏全局物品迁移信息用于自监督增强。该方法天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，转而设计了一种自适应数据重建范式，将其与长程物品依赖建模相结合，从而实现序列推荐中的信息性增强。大量实验表明，我们的方法显著优于最先进的基线模型，并且能够针对数据噪声和稀疏性学习到更准确的表示。我们实现的模型代码已公开发布于 https://github.com/HKUDS/GMRec。