Category Enhanced Dual View Contrastive Learning for Session-Based Recommendation

Xingfan Shi, Yuliang Shi, Jihu Wang, Hongfeng Sun, Hui Liu, Xinjun Wang, Zhiyong Chen

Abstract: Session-based recommendation aims to predict the next item based on users’ behavior sequence within a short time. Traditional session-based recommendation models usually assume that there exists only one type of interaction between users and items and fails to consider the impact of multiple types of behaviors. Although some recent studies have proposed to utilize different types of behaviors, they still have some challenges. First, they do not consider the impact of category information on user preferences. Secondly, they do not leverage the complementary information between multiple behaviors. To overcome the above challenges, we propose a novel Category Enhanced Dual View Contrastive Learning (CaDVCL) model, which explores the influence of item categories and multiple interaction behaviors on user interests. The model combines category sequence information and item sequence information to learn session representations through an attention mechanism and captures the correlation between different behaviors by maximizing the mutual information of session representations obtained from different behavioral views through contrastive learning. Extensive experiments on two public datasets show that CaDVCL can outperform the state-of-the-art models.