Dual-channel context-aware contrastive learning graph neural networks for session-based recommendation

Abstract

Session-based recommendation (SR) aims to predict the next most likely interaction item based on the current sequence of anonymous behaviors. How to learn short- and long-term user preferences is the key to SR research. However, current research ignores the impact of contextual information on users’ short- and long-term preferences when obtaining user preferences. Herein, we propose a Dual-Channel Context-aware Contrastive Learning Graph Neural Networks (DCC-GNN) model for SR. DCC-GNN constructs a time-aware session graph representation learning channel, modeling sessions with temporal context information to learn users’ short-term preferences. To better capture users’ long-term preferences, it also constructs a position correction global graph representation learning channel and uses global session information to learn users’ long-term preferences. To address the issue of data sparsity, contrastive learning techniques are employed to both channels for data augmentation. Finally, a linear combination of the dual-channel session representations serves as the user’s ultimate preference for accurate recommendations. Herein, we performed extensive experiments on three real-world datasets. Experimental results reveal that the performance of the proposed DCC-GNN model demonstrates a considerable improvement compared to baseline models.