Self-supervised contrastive learning, which directly extracts inherent data correlations from unlabeled data, has been widely utilized to mitigate the data sparsity issue in sequential recommendation. The majority of existing methods create different augmented views of the same user sequence via random augmentation, and subsequently minimize their distance in the embedding space to enhance the quality of user representations. However, random augmentation often disrupts the semantic information and interest evolution pattern inherent in the user sequence, leading to the generation of semantically distinct augmented views. Promoting similarity of these semantically diverse augmented sequences can render the learned user representations insensitive to variations in user preferences and interest evolution, contradicting the core learning objectives of sequential recommendation. To address this issue, we leverage the inherent characteristics of sequential recommendation and propose the use of context information to generate more reasonable augmented positive samples. Specifically, we introduce a context-aware diffusion-based contrastive learning method for sequential recommendation. Given a user sequence, our method selects certain positions and employs a context-aware diffusion model to generate alternative items for these positions with the guidance of context information. These generated items then replace the corresponding original items, creating a semantically consistent augmented view of the original sequence. Additionally, to maintain representation cohesion, item embeddings are shared between the diffusion model and the recommendation model, and the entire framework is trained in an end-to-end manner. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed method.

翻译：自监督对比学习通过直接从无标注数据中提取内在关联，已被广泛应用于缓解序列推荐中的数据稀疏问题。现有方法大多通过随机增强为同一用户序列创建不同的增强视图，随后最小化它们在嵌入空间中的距离以提升用户表示质量。然而，随机增强往往会破坏用户序列固有的语义信息与兴趣演化模式，导致生成语义差异显著的增强视图。推动这些语义各异的增强序列之间的相似性，可能使学习到的用户表示对用户偏好及兴趣演化的变化不敏感，这与序列推荐的核心学习目标相悖。为解决这一问题，我们利用序列推荐的内在特性，提出借助上下文信息生成更合理的增强正样本。具体而言，我们提出一种面向序列推荐的上下文感知扩散对比学习方法。给定用户序列，本方法选取特定位置，并采用上下文感知扩散模型在上下文信息引导下为这些位置生成替代项目。这些生成的项目随后替换对应的原始项目，形成与原始序列语义一致的增强视图。此外，为保持表示一致性，扩散模型与推荐模型共享项目嵌入，且整个框架以端到端方式进行训练。在五个基准数据集上的大量实验验证了所提方法的优越性。