Sequential recommendation based on multipair contrastive learning with informative augmentation

To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity-such as short historical user behaviour sequences and limited information-this paper proposes a sequential recommendation model based on multipair contrastive learning with informative augmentation (IA-MPCL). The model aims to better learn user preference representations. Initially, a self-attention network is utilized to maintain the intrinsic relevance of the original sequences and introduce virtual interaction items for short sequences to achieve informative enhancement. Subsequently, multiple positive samples are generated by data augmentation methods to form multiple pairs of positive and negative samples. A multipair contrastive loss is constructed to eliminate the negative impact of fake positive and negative samples on the training process of the self-attention network. Finally, an adaptive loss weighting mechanism is proposed to dynamically regulate the role of the contrastive loss during multitask training. Through comparison experiments involving baseline methods and experiments conducted on datasets with different sparsity levels, the results show that IA-MPCL achieves significant improvements in terms of both recommendation accuracy and data sparsity resistance.