A Recommendation Algorithm Based on Cross-View Contrastive Learning

Graph Neural Networks (GNNs), which possess the ability to capture the connectivity and topological features between nodes in a graph structure, have been widely used in the field of Knowledge Graph (KG)-enhanced recommendation. However, the advantages of GNNs are largely limited by the quality of the introduced KG. For example, (1) in real recommendation scenarios, often a small number of popular items interact with a large number of users, which leads to sparse supervisory signals; (2) approaches using GNNs mostly focus on coarse-grained relationship modeling under the global structure and are susceptible to the interference of neighboring noisy information in the KG. Inspired by self-supervised learning, we propose a recommendation algorithm based on cross-view contrastive learning and apply it to a KG-enhanced recommendation task. By promoting mutual supervision of node features in different semantic spaces under local and global structures, the algorithm learns discriminative node representations and reduces noise interference during iterative propagation. Meanwhile, we utilize multi-task learning to promote co-training and shared learning between the recommendation task and contrastive learning to improve the overall generalization ability and the quality of recommendations. To verify the effectiveness of the proposed work, we conduct experiments on two public datasets, MovieLens and Book-Crossing. The experimental results show that our recommendation algorithm outperforms the baseline model in most cases in terms of AUC, recall@k, and F1, and the recommendation performance is improved by 1.7% -10.4% x.