Help from meta-path: node and meta-path contrastive learning for recommender systems

Recently, contrastive learning alleviates data sparsity issues and improves the performance of the Graph Neural Network (GNN) recommender models by employing graph structure dropout augmentations. However, these models still face following limitations: (1) Information loss. Dropout may discard helpful information. (2) Insufficient utilization of path-level information. Meta-path is carried numerous high-order information, which has not been well considered in these models. To this end, in this paper, we propose a novel framework, Node and Meta-Path Contrastive Learning for Recommender Systems (NPCRS), which utilizes meta-path to capture path-level information for model learning. Specifically, our approach first generates a meta-path view on the user-item bipartite graph by leveraging meta-path instead of random dropout. Then, we learn the node representation on a user-item bipartite graph and meta-path view to capture both node and path-level information simultaneously. Further, a multi-positive sample mechanism is introduced to define positive and negative samples for contrastive learning. Finally, NPCRS utilizes contrastive learning to learn a more informative node representation. We evaluate the proposed model using three real-world datasets and our experimental results show that our model significantly outperforms the state-of-the-art approaches.