Hierarchical Contrastive Learning with Multiple Augmentations for Sequential Recommendation

Sequential recommendation aims to predict users' next actions by analyzing their historical behavior. Lately, contrastive learning has become prominent in this domain, especially when user interactions with items are sparse. Although data augmentation methods have flourished in fields like computer vision, their potential in sequential recommendation remains under-explored. Thus, we present Hierarchical Contrastive Learning with Multiple Augmentations for Sequential Recommendation (HCLRec), a novel framework that harnesses multiple augmentation techniques to create diverse views on user sequences. This framework systematically employs existing augmentation techniques, creating a hierarchy to generate varied views. First, we augment the input sequences to various views using multiple augmentations. Through the continuous composition of these augmentation methods, we formulate both low-level and high-level view pairs. Second, an effective sequence-based encoder is used to embed input sequences, complemented by the supplementary blocks to capture users' nonlinear behaviors, which are further varied by augmentations. Input sequences are routed to subsequent layers based on the number of augmentations applied, helping the model discern intricate sequential patterns intensified by these augmentations. Finally, contrastive losses is calculated between view pairs of the same level within each layer. This allows the encoder to learn from the contrastive losses between augmented views of the same level, and the gap caused by different information between the low-level views and high-level views by multiple augmentations is reduced. In evaluations, HCLRec outperforms state-of-the-art methods by up to 7.22% and demonstrates its effectiveness in handling sparse data.