  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.
