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（CL）通过信息丰富的自监督信号增强序列推荐（SR）模型的训练。现有方法通常依赖数据增强策略构建正样本并促进表征不变性。诸如物品重排序和物品替换等策略可能无意中改变用户意图。基于监督对比学习（SCL）的方法通过选择同目标序列（具有相同目标物品的交互序列）形成正样本，为基于增强的CL方法提供了替代方案。然而，SCL方法受限于同目标序列的稀缺性，导致缺乏足够的对比学习信号。本文提出使用相似序列（具有不同目标物品）作为额外正样本，并引入面向序列推荐的相对对比学习（RCL）框架。RCL包含双层正样本选择模块和相对对比学习模块：前者选择同目标序列作为强正样本，选择相似序列作为弱正样本；后者采用加权相对对比损失，确保每个序列的表征更接近其强正样本而非弱正样本。我们将RCL应用于两种主流的深度学习SR模型，实验结果表明，在五个公开数据集和一个私有数据集上，RCL平均可获得比最先进SR方法高4.88%的性能提升。