Heterogeneous Graph Contrastive Learning with Attention Mechanism for Recommendation

Existing recommendation algorithms based on heterogeneous graphs often face performance limitations due to the sparsity and nonlinearity of the heterogeneous graph structure and semantic information, which hinders the full exploitation of the association information between users and items. In order to tackle these challenges and improve the quality of user and item feature representations, a Heterogeneous Graph Contrastive Learning Recommendation algorithm based on Attention Mechanism (HAMRec) has been proposed. To enhance the robustness of graph representations, this algorithm introduces an unsupervised contrastive learning approach and utilizes attention mechanisms on top of graph neural networks to extract both local and global information from different heterogeneous graphs. Considering the varying impact of heterogeneous auxiliary information on recommendation results in real-life scenarios, HAMRec employs personalized knowledge transfer to enhance self-supervised learning. Through a large number of experiments, it has been proven that HAMRec surpasses existing baseline models in recommendation tasks, proving its effectiveness and superiority.