Time-Aware Multibehavior Contrastive Learning for Social Recommendation

Abstract

The social relationships among users can be effectively represented using graph structures, which has led to increasing interests in utilizing graph neural networks (GNNs) for social recommendation.However, there are still some inevitable issues in the existing methods: 1) The problem of sparse supervision signals in the GNNbased recommendation models has not been well addressed.2) The existing social recommendation methods often neglect the guiding effect of the auxiliary behaviors on the target behaviors, where only the single target behavior data are used for model training.3) In the GNN-based social recommendation algorithms, the dynamics of recommendations are rarely considered.To address these issues, this article proposes a time-aware multibehavior contrastive learning framework.To achieve better-personalized recommendation, we perform representation learning from multiview perspectives, incorporating temporal information and multibehavior interactions into the social recommendation.A time-aware GNN is then designed to model the dynamic dependency relationships between users and items, by which the dynamics of recommendations can be enhanced.Meanwhile, we propose a multibehavior contrastive learning framework to rationalize the use of multibehavioral data and address the problem of sparse supervision signals.Extensive experiments on three real-world