Sequential recommendation addresses the issue of preference drift by predicting the next item based on the user's previous behaviors. Recently, a promising approach using contrastive learning has emerged, demonstrating its effectiveness in recommending items under sparse user-item interactions. Significantly, the effectiveness of combinations of various augmentation methods has been demonstrated in different domains, particularly in computer vision. However, when it comes to augmentation within a contrastive learning framework in sequential recommendation, previous research has only focused on limited conditions and simple structures. Thus, it is still possible to extend existing approaches to boost the effects of augmentation methods by using progressed structures with the combinations of multiple augmentation methods. In this work, we propose a novel framework called Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation(HCLRec) to overcome the aforementioned limitation. Our framework leverages existing augmentation methods hierarchically to improve performance. By combining augmentation methods continuously, we generate low-level and high-level view pairs. We employ a Transformers-based model to encode the input sequence effectively. Furthermore, we introduce additional blocks consisting of Transformers and position-wise feed-forward network(PFFN) layers to learn the invariance of the original sequences from hierarchically augmented views. We pass the input sequence to subsequent layers based on the number of increment levels applied to the views to handle various augmentation levels. Within each layer, we compute contrastive loss between pairs of views at the same level. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art approaches and that HCLRec is robust even when faced with the problem of sparse interaction.

翻译：序列推荐通过预测用户历史行为的下一个项目来解决偏好漂移问题。近期，基于对比学习的方法在稀疏用户-项目交互场景下展现出显著推荐效果。值得注意的是，多种增强方法的组合在不同领域（尤其是计算机视觉）中已被证实有效。然而，在序列推荐的对比学习框架中应用增强时，先前研究仅关注有限条件和简单结构。因此，仍可通过采用复合增强方法的进阶结构来扩展现有方法，以提升增强效果。本文提出名为HCLRec（基于多重增强的分层对比学习序列推荐）的新型框架以突破上述局限。该框架分层利用现有增强方法提升性能，通过连续组合增强方法生成低层与高层视图对。我们采用基于Transformer的模型高效编码输入序列，并引入由Transformer层和位置前馈网络层构成的附加模块，从分层增强视图中学习原始序列的不变性。根据视图所应用的增量层级数量，将输入序列传递至对应后续层级以处理不同增强级别。在各层级内，计算同层视图对之间的对比损失。大量实验表明，本方法显著优于现有最优方案，且HCLRec在面临稀疏交互问题时仍保持鲁棒性。