Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.

翻译：对比学习（CL）通过提供丰富的自监督信号，有益于序列推荐模型的训练。现有方法通常采用通用的序列数据增强策略来生成正样本对，并鼓励其表示具有不变性。然而，由于用户行为序列的内在特性，某些增强策略（如物品替换）可能导致用户意图发生改变。对所有增强策略不加区分地学习不变表示可能并非最优方案。为此，我们提出用于序列推荐的等变对比学习（ECL-SR），该方法赋予序列推荐模型强大的判别能力，使用户行为学习到的表示对侵入性增强（如物品替换）保持敏感，而对温和增强（如特征级丢弃掩蔽）保持不敏感。具体而言，我们利用条件判别器来捕捉因物品替换导致的行为差异，从而促使用户行为编码器对侵入性增强具有等变性。在四个基准数据集上的综合实验表明，所提出的ECL-SR框架相较于最先进的序列推荐模型取得了有竞争力的性能。源代码可在https://github.com/Tokkiu/ECL获取。