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）通过高阶物品依赖建模在序列推荐中取得了改进的性能，但在标签稀缺场景下它们可能面临表示能力不足的问题。为解决标签不充分的问题，对比学习近年来在相关方法中备受关注，其通过嵌入对比进行数据增强以实现自监督学习。然而，由于对比视角生成策略的人工设计特性，现有的对比学习增强模型：i) 难以在多样化的序列推荐任务中保持一致的性能；ii) 可能无法免疫用户行为数据噪声。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序列推荐系统（MAERec），其能够自适应且动态地提取全局物品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角的过度依赖问题。相反，我们设计了一种自适应数据重建范式，并将其与长程物品依赖建模相结合，用于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，并且能够学习到更准确的表示以应对数据噪声和稀疏性。我们实现的模型代码可在 https://github.com/HKUDS/MAERec 获取。