Item Attribute-Aware Contrastive Learning for Sequential Recommendation

Abstract

Sequential recommendation aims to predict users’ next interaction items based on their historical interaction sequences, however, the problem of sparse user behavior and ineffective use of item attribute information makes it difficult to learn high-quality representations of user preferences. To address this problem, inspired by recent advances in contrastive learning techniques, we propose a novel Item Attribute-aware Contrastive Learning framework for Sequential Recommendation, named IACL4SR, which differs from previous contrastive learning-based sequential recommendation approaches by incorporating item attribute information in user behavior sequences to build an augmented view of users’ fine-grained preferences for item attributes, thereby capture the association between user and item attributes in the sequence transformation model. Specifically, we devise a dual-strategy relay data augmentation method to model user sequences and item attribute sequences, while we modify the fusion method of item attribute embedding in the self-attentive mechanism to obtain more accurate user representations. Finally, we jointly train and optimized the main sequential recommendation task and auxiliary contrastive learning task. Extensive experiments on three widely used real datasets show that IACL4SR achieves more advanced recommendation performance than existing baseline methods.