Contrastive Learning (CL) enhances the training of sequential recommendation (SR) models through informative self-supervision signals. Existing methods often rely on data augmentation strategies to create positive samples and promote representation invariance. Some strategies such as item reordering and item substitution may inadvertently alter user intent. Supervised Contrastive Learning (SCL) based methods find an alternative to augmentation-based CL methods by selecting same-target sequences (interaction sequences with the same target item) to form positive samples. However, SCL-based methods suffer from the scarcity of same-target sequences and consequently lack enough signals for contrastive learning. In this work, we propose to use similar sequences (with different target items) as additional positive samples and introduce a Relative Contrastive Learning (RCL) framework for sequential recommendation. RCL comprises a dual-tiered positive sample selection module and a relative contrastive learning module. The former module selects same-target sequences as strong positive samples and selects similar sequences as weak positive samples. The latter module employs a weighted relative contrastive loss, ensuring that each sequence is represented closer to its strong positive samples than its weak positive samples. We apply RCL on two mainstream deep learning-based SR models, and our empirical results reveal that RCL can achieve 4.88% improvement averagely than the state-of-the-art SR methods on five public datasets and one private dataset.

翻译：对比学习通过信息性自监督信号增强了序列推荐模型的训练。现有方法通常依赖数据增强策略来创建正样本并促进表示不变性。诸如物品重排序和物品替换等策略可能会无意中改变用户意图。基于监督对比学习的方法通过选择相同目标序列（具有相同目标物品的交互序列）来形成正样本，从而寻找了基于数据增强的对比学习方法的替代方案。然而，基于监督对比学习的方法面临相同目标序列稀缺的问题，因此缺乏足够的对比学习信号。在这项工作中，我们提出使用相似序列（具有不同目标物品）作为额外的正样本，并引入了一种用于序列推荐的相对对比学习框架。该框架包含一个双层正样本选择模块和一个相对对比学习模块。前者选择相同目标序列作为强正样本，并选择相似序列作为弱正样本。后者采用加权相对对比损失，确保每个序列的表示更接近其强正样本而非弱正样本。我们将相对对比学习应用于两个主流的基于深度学习的序列推荐模型，实证结果表明，在五个公开数据集和一个私有数据集上，相对对比学习平均比最先进的序列推荐方法提升了4.88%。